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 CONTRACTING AND CONTRACT LAW
IN THE AGE OF ARTIFICIAL INTELLIGENCE
This book provides original, diverse, and timely insights into the nature, scope,
and implications of Artificial Intelligence (AI), especially machine learning and
natural language processing, in relation to contracting practices and contract law.
The chapters feature unique, critical, and in-depth analysis of a range of topical
issues, including how the use of AI in contracting affects key principles of contract
law (from formation to remedies), the implications for autonomy, consent, and
information asymmetries in contracting, and how AI is shaping contracting
practices and the laws relating to specific types of contracts and sectors.
The contributors represent an interdisciplinary team of lawyers, computer
scientists, economists, political scientists, and linguists from academia, legal prac-
tice, policy, and the technology sector. The chapters not only engage with salient
theories from different disciplines, but also examine current and potential real-
world applications and implications of AI in contracting and explore feasible legal,
policy, and technological responses to address the challenges presented by AI in
this field.
The book covers major common and civil law jurisdictions, including the EU,
Italy, Germany, UK and the US. It should be read by anyone interested in the
complex and fast-evolving relationship between AI, contract law, and related areas
of law such as business, commercial, consumer, competition, and data protection
laws.
Contracting and Contract
Law in the Age of Artificial
Intelligence

Edited by
Martin Ebers
Cristina Poncibò
and
Mimi Zou
 HART PUBLISHING
Bloomsbury Publishing Plc
Kemp House, Chawley Park, Cumnor Hill, Oxford, OX2 9PH, UK
1385 Broadway, New York, NY 10018, USA
29 Earlsfort Terrace, Dublin 2, Ireland

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statement in it can be accepted by the authors, editors or publishers.
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of the Open Government Licence v3.0 (http://www.nationalarchives.gov.uk/doc/
open-government-licence/version/3) except where otherwise stated.
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http://eur-lex.europa.eu/, 1998–2022.

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Library of Congress Control Number: 2022933882
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and the option to sign up for our newsletters.
 PREFACE

Today we are witnessing the rapid development of artificial intelligence (AI), which
is enabling software agents to carry out a growing variety of transactions inde-
pendent of human intervention. Some of these transactions include comparative
pricing, negotiating contractual terms, and buying and selling goods and services.
A broad question underlying numerous contributions to this book is whether the
use of AI in contracting can support or, perhaps in the near future, even replace
humans when it comes to entering into, drafting, and executing contracts.
AI and other new technologies such as blockchain-based smart contracts
can remove various forms of human intervention from contractual transactions.
Accordingly, the law must find a way to enforce transactions between AI and a
human contracting party where, strictly speaking, there is no ‘consensus’ or ‘meet-
ing of the minds’ in the traditional definition of these concepts. This is crucial
for machine-generated transactions where human intervention is absent from at
least one side of the transaction. Indeed, AI systems and tools in use today can
enter into transactions whereby the details of such transactions are unknown
to humans. In some cases, contracts are concluded with content that was never
explicitly intended, foreseen, or authorised by their programmers or users.
In these cases, such AI systems are not mere instruments for contractual transac-
tions. They are not static vending machines that merely serve as conduits through
which contractual transactions take place.
While extra-contractual liability for AI has been widely analysed by scholars in
various jurisdictions, the specific impact of AI on our understanding of contract-
ing and contract law in this new age remains largely unexplored. This book thus
provides original, diverse, and timely insights into the nature, scope, and impact of
AI – particularly machine learning and natural language processing – in relation
to contracting practices and contract law.
The chapters of this book provide an in-depth analyses of a range of topical
issues, including how the use of AI in contracting affects key principles of contract
law (from formation to remedies); the impact of AI on autonomy, consent, and
information asymmetries in contracting; and how AI is shaping contracting prac-
tices and the laws relating to specific types of contracts and sectors. The chapters
not only examine these issues by drawing on key theories from different disci-
plines, but also real-world applications of AI in contracting. The chapters further
explore possible legal, policy, and technological responses to the challenges that
AI poses in this field.
The contributors to this book represent an interdisciplinary team of lawyers,
computer scientists, economists, political scientists, and linguists from academia,
vi Preface

legal practice, policy, and the technology sector. Furthermore, the book covers
major common law and civil law jurisdictions that are at the forefront of AI devel-
opment and application globally, including the European Union (EU), the United
Kingdom (UK), and the United States (US).

AI in Contractual Decision-making and Drafting

Numerous chapters of the book explore inter alia how AI may help (and in some
cases, replace) humans in contractual decision-making and drafting.
AI may be used in the pre-contractual phase to assess whether or not a party
should enter into a contract at all. This involves several aspects. AI systems can
encourage people to enter into a contract by identifying the potential needs to
be met by the contract. To this end, some companies and banks are using AI to
analyse prospective corporate mergers or takeovers. In the same vein, a company
may use AI for location-based intelligence to predict specific product purchases by
customers in a particular locality.
AI can help to enhance decision-making processes by analysing large datasets
that reveal a range of contractual risks. AI systems make it possible to undertake
due diligence of hundreds of thousands of documents in a very short time, and to
identify issues that need to be flagged to a human reviewer. AI-driven risk analysis
tools can also be deployed to assess individual risk profiles. Several AI systems are
already in use by financial and insurance institutions to evaluate their customers
in relation to whether or not a loan may be granted or an insurance policy taken
out, and on what terms.
In addition to risk analysis, AI can also improve the analysis of transaction
costs. Economists have examined how people decide between different contrac-
tual alternatives on the basis of the related transaction costs. AI-driven pricing
software can facilitate this decision-making process through intelligent data
analytical tools.
Moreover, AI can help with the analysis and drafting of contract clauses.
AI-driven contract review software, which analyses contract clauses, makes it
possible to identify certain risks. Applications of this type are already widespread
in several jurisdictions such as the US and UK. Nevertheless, many chapters of this
book point out that automated drafting of contracts is still in its infancy. In fact,
most software solutions currently available are based on standard templates that
have been adapted to the circumstances of an individual case with the help of ques-
tionnaires created by virtual robot assistants, so-called ‘chatbots’. Another method
involves writing a contract directly in code to integrate with a computer system for
managing and executing contracts. The contract simultaneously becomes part of
the database that will later be used to train the AI system for drafting contracts.
Finally, AI can support decision-making during the lifetime of a contract.
Although the use of AI is not yet widespread in this area, it is foreseeable that
 Preface vii

relevant use cases will become more common. Such applications may be able to
evaluate the advantages and disadvantages of certain options available to parties
during the performance phase of a contract. AI may even help parties decide
whether or not a breach of contract is more advantageous than performing the
contract by predicting the respective costs of performance and non-performance.

AI in Contract Formation and Execution

The chapters of this book also examine use cases of AI in contract formation and
execution. We mention two scenarios here: first, the role of AI as a contractual
agent, ie, acting on behalf of humans and/or other computers; second, the poten-
tial role of AI (in the future) as an autonomous contractual party.
First, AI can be considered an agent in contract law in the context of computer
systems that can be programmed by humans to make certain contractual deci-
sions. Thus, a machine can decide whether or not to conclude a contract if specific
predetermined conditions are met. This is the case with algorithmic trading, which
accounts for a significant proportion of financial transactions globally. Based on
hundreds of parameters, a computer system can decide the buying or selling of
a particular financial product at a certain price, in a certain volume, at a certain
time, in a certain market, and under certain conditions. Moreover, AI systems may
also decide when and how to perform a contract. The mechanical execution of
contracts is well-known, for example, in beverage vending machines. In such a
case, no intelligence is required. The same applies to smart contracts involving
software code that automates the execution of a contract.
Second, in the future, AI systems may become autonomous contractual parties.
This is where AI no longer acts as an agent for others, but for itself. It is autonomous,
in the literal sense of auto-nomos, which is governed by its own laws. To be party
to a contract, one must generally have the capacity to contract, ie, possess legal
personality. In a resolution adopted in 2017, the European Parliament supported
the idea of ‘creating a specific legal status for robots in the long run, so that at
least the most sophisticated autonomous robots could be established as having the
status of electronic persons responsible for making good any damage they may
cause, and possibly applying electronic personality to cases where robots make
autonomous decisions or otherwise interact with third parties independently’.
At the dawn of a new era, AI has the potential to radically impact decision-
making in contracting, with significant implications for contract law. This raises
serious research questions that numerous contributors of this book have analysed
from different perspectives. For example, with what and/or whom is the human
contracting party transacting? From a civil law perspective, a question arises as to
whether the contract is concluded by the interposition of a ‘thing’ or a ‘person’?
Intuitively, we may see the interposition of a ‘thing’ in this case: the contract is
concluded by AI as an instrument in the same way that a phone or computer may
viii Preface

be used to enter into a contract. However, the use of AI here involves a certain level
of autonomy in contracting. So, should we conclude that, more than the interposi-
tion of a ‘thing’, it is AI that actually realises the interposition of a form of ‘person’?
In both civil and common law jurisdictions, the law already grants legal personal-
ity to non-human contracting parties such as corporations.
The possibility to grant legal personality to AI systems is, however, disputed
among legal scholars. If we classify the AI system involved in the conclusion of
a contract as an ‘agent’ acting on behalf of the system’s user (the ‘principal’), this
could give rise to some practical problems. Notably, the expression of the user’s will
may be limited due to the autonomy of the AI. Depending on the circumstances,
the distribution of risks between the ‘principal’ and ‘agent’ may differ. For instance,
if the AI system itself does not function properly or is poorly programmed and
makes bad decisions, the risks must be assumed by the developer. However, if the
bad decisions made by the AI system reflect defects in a poorly designed contract,
risk sharing between the parties could be considered. In this latter case, it may be
necessary to provide certain legal protection to some contracting parties such as
consumers. For example, consumers may be given the right to an extended period
of time to cancel the contract, which would enable consumers to regain their deci-
sion-making power to some extent.
Third and important, Alan Turing, the father of modern computing, basi-
cally studied the following question: can machines think? In terms of contract law
theory, the relevant questions are: can AI systems make contracts, or more broadly,
make decisions instead of a contracting party? Does such a machine have a place
in modern contract law? The use of AI in contracting has the potential to shake up
core aspects of contract law theory, such as agreement, consensus, and the inten-
tion of the parties. For instance, even if we were to decide on policy grounds to
impose contractual liability on individuals who use or program AI systems, it is
difficult to regard these individuals as ‘offerors’ in the conventional understanding
of this concept. Such individuals have no idea if or when the system will make an
offer; they have no idea what the terms of the offer will be; and they have no easy
way to influence negotiations once the transaction is underway. Moreover, if we
consider relational contract theories, such as the work of Ian MacNeil, contracts
should be studied and understood as (human) relations rather than as discrete
transactions. AI systems have the potential of replacing the relational aspects of
contracts, such as trust, promise, consent, and enforcement.

Conclusion
The chapters in this book highlight the multifaceted and complex nature and
implications of AI in relation to contracting and contract law. There is a dominant
discourse surrounding the use of AI to drive efficiency in contracting, such as cost
reduction, process optimisation, and disintermediation. This economic logic can
 Preface ix

directly clash with the law’s own logic and values such as the importance of good
faith, fairness, and protection of the weaker party to a contract.
We conclude with considering the risk of viewing AI as displacing the human
elements of contracts. Contracts are created by and for humans to undertake a
range of activities, even if contracts entail (non-human) legal persons such as
corporations. Furthermore, contract law pays attention to the human elements
of contracting, such as defects in consent and the intent of the parties. It remains
to be seen whether AI will marginalise these human elements of contracting and
contract law. As AI develops, it may remove humans from important processes of
decision-making in relation to contracting. This is already reflected in algorithmic
trading whereby the assumption is that humans cannot make decisions in micro-
seconds (that is, in millionths of a second) when faced with vast amounts of data.
There is a real danger in heralding the superiority of AI over humans in achieving
optimal economic outcomes in contracting. We believe that the human elements
are what essentially make contracts and contract law ‘living institutions’ that serve
a range of important market and non-market purposes.

Martin Ebers
Cristina Poncibò
Mimi Zou
 ACKNOWLEDGEMENTS

The contributions to this book are primarily based on the presentations held at
the virtual conference ‘Contracting and Contract Law in the Age of Artificial
Intelligence’ at the University of Turin on the 11th and 12th of February 2021.
The conference received funding by the Journal of Artificial Intelligence (AIJ) under
the AIJ’s 22nd call for Sponsorship.
We are grateful to the people who made this conference possible and would
like to thank especially Prof. Luca Cagliero (Department of Control and Computer
Engineering of Turin Polytechnic), Prof. Massimo Durante (Department of Law
of the University of Turin) and Dr. Willem Wiggers (Weagree) for chairing the
sessions of our conference.
In addition, we would like to express our gratitude to the Interdepartmental
Research Center for Artificial Intelligence of the University of Eastern Piedmont,
the Department of Control and Computer Engineering of Turin Polytechnic, The
Turin Observatory on Economic Law & Innovation (TOELI) and the Robotics &
AI Law Society (RAILS) for supporting the conference and this book.
Last but not least, we would like to thank the wonderful team at Hart, espe-
cially Rosemarie and Roberta, for their continued support and patience in helping
us at every step of the way in publishing this book.
 NOTES ON CONTRIBUTORS

Paola Aurucci is a research fellow at the Department of Management of the

University of Turin. For several years she has been a researcher at the University
of Eastern Piedmont and from 2016 she has been collaborating with the Faculty
of Philosophy of Law at the University of Turin (Department of Law). Since 2017,
she has collaborated as a researcher and law consultant at the Center for Advanced
Technology in Health & Wellbeing of the San Raffaele Hospital in Milan. Since
2021, she has collaborated as a Privacy Consultant with the Ethical Committee of
the Hospital San Raffaele.
Luca Cagliero is Associate Professor at the Politecnico di Torino, where he
teaches B.Sc., M.Sc., Ph.D., and Master-level courses on database and data ware-
house design, data mining and Machine Learning techniques, and Deep Natural
Language Processing. He is currently a member of the Centro Studi@PSQL board
for data-driven planning of strategic, university-level actions. He is also the
academic advisor of both incoming and outgoing students for the Data Science and
Engineering exchange program. Since 2017, he has joined the SmartData@Polito
interdepartmental research center. His research activities are mainly devoted to
studying innovative data mining and machine learning solutions and algorithms,
with a particular emphasis on text summarisation, classification of structured and
semi-structured data, and multiple-level pattern mining. Luca is associate editor of
the Expert Systems With Applications and Machine Learning With Applications
journals, both edited by Elsevier. He has co-authored 100+ scientific publications.
He has been the recipient of the Telecom Italia Working Capital 2011 Research
Grant. He has coordinated scientific collaborations with big companies (eg, F.C.A.
SpA, Telecom SpA, Telepass SpA) and with various SMEs (eg, Tierra SpA, Reale
Mutua Assicurazioni, Pattern Srl).
Giuseppe Colangelo is a Jean Monnet Professor of European Innovation Policy
and an Associate Professor of Law and Economics at University of Basilicata.
Since 2017, he has been a Transatlantic Technology Law Forum (TTLF) Fellow at
Stanford University Law School. He also serves as Adjunct Professor of Markets,
Regulation and Law, and of Competition and Markets of Innovation at LUISS. His
primary research interests are related to innovation policy, intellectual property,
competition policy, market regulation, and economic analysis of law.
Martin Ebers is Associate Professor of IT Law at the University of Tartu (Estonia)
and as ‘Privatdozent’ permanent research fellow at the Humboldt University of
Berlin (Germany). He is co-founder and president of the Robotics & AI Law
xvi Notes on Contributors

Society (RAILS). Martin is the author and editor of 16 books and over 120 articles
published in national and international journals. In addition to research and
teaching, he has been active in the field of legal consulting for many years. His
main areas of expertise and research are IT law, liability and insurance law, and
European and comparative law. In 2020, he published the books ‘Algorithms and
Law’ (Cambridge University Press), ‘Rechtshandbuch Künstliche Intelligenz und
Robotik’ (C.H. Beck) and ‘Algorithmic Governance and Governance of Algorithms’
(Springer Nature).
Agnieszka Jabłonowska is an assistant professor at the Institute of Law Studies of
the Polish Academy of Sciences in Warsaw where she works on the project ‘Citizen
empowerment through online terms of service review: an automated transpar-
ency assessment by explainable AI’. Prior to that she was a Max Weber Fellow
at the European University Institute in Florence, a research assistant and a PhD
researcher at the University of Lodz. Her academic interests lie at the intersec-
tion of law and technology, with a focus on consumer protection, online platforms
and artificial intelligence. Agnieszka has been involved, among others, in the
European Law Institute’s project ‘Model rules on online platforms’, the European
University Institute’s project ‘Artificial intelligence systems and consumer law &
policy’ (ARTSY) and the project ‘Consumer protection and artificial intelligence.
Between law and ethics’ carried out at the University of Lodz.
Aleksei Kelli is Professor of Intellectual Property Law (the University of Tartu,
Estonia). He is a member of the court of honour of the Estonian Bar Association
and CLARIN ERIC Legal and Ethical Issues Committee. Aleksei holds a doctor-
ate (PhD in Law) from the University of Tartu (2009). Aleksei has acted as the
Head of an Expert Group on the Codification of the Intellectual Property Law
(2012–2014, the Ministry of Justice of Estonia). He was the principal investigator
in the Programme for Addressing Socio-economic Challenges of Sectoral R&D
in the field of industrial property (2017–2018) and open science (2016–2017).
Dr Kelli managed a project to improve industry-academia cooperation and knowl-
edge transfer in the Ukraine (2015–2016) and was the leading intellectual property
expert in the research and innovation policy monitoring programme (2011–2015).
Dr Kelli was also a Member of the Team of Specialists on Intellectual Property
(2010–2013, the United Nations Economic Commission for Europe). He has taken
part in several EU and Estonian R&D projects as a leading IP, innovation, and data
protection expert. Dr Kelli has published numerous works on intellectual prop-
erty, innovation, personal data protection, knowledge transfer, cultural heritage
and related issues.
John Linarelli is Associate Dean for Academic Affairs and Professor of Law at
Touro University Jacob D. Fuchsberg Law Center in Central Islip, New York. He
was, until July 2020, Professor of Commercial Law at Durham University Law
School in the UK. His work on artificial intelligence draws connections between
moral psychology, philosophy of mind, and law. His many publications include
 Notes on Contributors xvii

‘Artificial Intelligence and Contract’, published in the Uniform Law Review. He is

the shared recipient of the European Society of International Law Book Award for
2019. He is an elected member of the American Law Institute and a fellow of the
European Law Institute. He is series co-editor of Hart Studies in Commercial and
Financial Law. He holds a PhD in Philosophy from the University of California
Riverside and a PhD in Law from King’s College London.
Krister Lindén is Research Director of Language Technology at the Department
of Digital Humanities at the University of Helsinki. He is National Coordinator of
FIN-CLARIN and has served as Chair of the CLARIN Strategy and Management
Board and is Vice Chair of the CLARIN National Coordinators’ Forum, as well as
participates in the CLARIN Legal and Ethical Issues Committee and the CLARIN
Interoperability Committee. He holds a doctorate (PhD in Language Technology)
from the University of Helsinki in 2005. He serves as National Anchor Point in
ELRC and is Finland’s representative in ELG. He has experience as CEO and CTO
of the commercial company Lingsoft Inc. with the successful application and
completion of several EU projects. He is very familiar with current methods and
branches within language and speech technology and has directed a number of
research projects funded by the Academy of Finland and is currently Vice-Team
Leader of the Center of Excellence in Ancient Near Eastern Empires. In addition
to having developed software for processing resources for the national languages
of Finland, he has published more than 140 peer-reviewed scientific publications.
Megan Ma is a Residential Fellow at the Stanford Center for Legal Informatics
(CodeX). Her research considers the limits of legal expression, in particular how
code could become the next legal language. Her work reflects on the frameworks
of legal interpretation and its overlap in linguistics, logic, and aesthetic program-
ming. Megan is also the Managing Editor of the MIT Computational Law Report
and a Research Affiliate at Singapore Management University in their Centre for
Computational Law. As well, she is finishing her PhD in Law at Sciences Po and
was a lecturer there, having taught courses in Artificial Intelligence and Legal
Reasoning, Legal Semantics, and Public Health Law and Policy. She has previ-
ously been a Visiting PhD at the University of Cambridge and Harvard Law School
respectively.
Silvia Martinelli is lawyer, Philosophiae Doctor (PhD), Research Fellow at
the University of Turin, Co-founder and Fellow at the Turin Observatory on
Economic Law and Innovation (TOELI), Fellow at the Information Society Law
Center (ISLC) and Strategic Research Manager in Data Valley. Graduated in Law
at the University of Milan and specialised in Legal Informatics, she obtained her
doctorate with merit from the University of Turin, with a thesis on platform econ-
omy and the responsibility of intermediary platforms, focusing on Uber, Airbnb,
Amazon and Ebay. In Data Valley she is Strategic Research Manager and deals
with research related to data-driven business models and the development of high-
tech solutions and services. She is also co-founder and member of the Editorial
xviii Notes on Contributors

Board of the Journal of Law, Market & Innovation (JLMI), TOELI Research
Papers coordinator, member of the Board of the Journal of Strategic Contracting
and Negotiation and of the Editorial Committee of the Law Reviews ‘Ciberspazio
e Diritto’, ‘Diritto, Mercato e Tecnologia’ and ‘Diritto di Internet’, Fellow of the
European Law Institute and of the Italian Academy of Internet Code, Member of
the European Law & Tech Network.
Giulio Messori is a co-founder at Sweet Legal Tech (SLT), an Italian Legal Tech
start up offering consulting, education and the integration of existing legal tech
solutions with the aim of transforming legal and administrative processes in
legal teams. Prior to SLT, he worked as a Legal Counsel at Chino.io, a company
providing cloud and IT infrastructure solutions to the healthcare sector and Data
Protection and IT law Senior Associate at CRCLEX, advising companies in the
Digital-Out-Of-Home, Digital Signage, Healthcare and IoT sector on Privacy, Data
Governance and Data Security compliance. In 2019, with two fellow colleagues at
Google Italy he co-founded develawpers.com, an Open-Coding Library dedicated
to the collection and illustration of coding cases in the world of law. Giulio holds
an LL.M. (Master of Laws) in Law of Internet Technology, obtained cum laude at
Bocconi University (Milan, Italy) and a Master’s Degree in Law with full marks at
the University of Bologna (Italy).
Monika Namysłowska is Professor of Law and Head of the Department of
European Economic Law at the University of Lodz, Poland. Her main areas of
research cover European, Polish and German Private Law, particularly IT law
and consumer law. She was visiting professor in Germany (Humboldt-University,
Berlin; Georg-August-University, Göttingen; University of Regensburg; University
of Münster), Italy (University of Naples Federico II), Spain (Universidad Publica de
Navarra in Pamplona) and Hungary (University of Szeged). She is principal investi-
gator in the project ‘Consumer Protection and Artificial Intelligence. Between Law
and Ethics’ funded by the National Science Centre in Poland (DEC/2018/31/B/
HS5/01169). She is local coordinator of TechLawClinics – an international project
(University of Nijmegen (NL), University of Lodz (PL), University of Krakow (PL),
University of Eastern Piedmont (IT)) on legal challenges and implications of digi-
tal technologies, supported by Erasmus+. She was member of the Advisory Board
of the President of the Office of Competition and Consumer Protection (UOKiK)
in Poland (2014–2016). She is expert in the Consumer Policy Advisory Group
established by the European Commission.
Carlo Rossi Chauvenet is Managing partner of CRCLEX, a law firm specialising
in IT Law and Privacy Law, and is appointed as Data Protection Officer (DPO)
in listed companies. Carlo is the coordinator of the Legal Clinic of the start-up
accelerator ‘Bocconi4Innovation’, co-founder of legal tech companies such as
‘Iubenda’, an automatic privacy policy generator, and ‘Sweet Legal Tech’, a consul-
tancy company in Legal Tech. Carlo is Adjunct professor of ‘Privacy Law and Data
Strategy’ at the LLm in Law of Internet Technology at Bocconi University, at the
 Notes on Contributors xix

Master for Corporate Counsels and at the Master in Open Innovation Management
at the University of Padova. He is also Chair of the ‘National Centre for IOT and
Privacy’ and manager of the ‘Data Valley’ initiative, a program dedicated to the
development of partnerships between PMI and digital Multinational companies.
Education: BA (hons.) in Law (Bocconi University), PHD (University of Padua),
LLM in International Corporate law (New York University School of law), LLM in
International Commerce (National University of Singapore).
Cristina Poncibò is Professor of Comparative Private Law at the Law Department
of the University of Turin, Italy and Visiting Professor (2021–2022) at the
Georgetown Law Center for Transnational Legal Studies in London. Cristina
is Fellow of the Transatlantic Technology Law Forum (Stanford Law School
and Vienna School of Law). She is a co-editor of the Cambridge Handbook of
Smart Contracts, Blockchain Technology and Digital Platforms (Cambridge
University Press, 2019, with L Matteo and M Cannarsa). Cristina is a member
of the International Association of Comparative Law and Delegate of the Law
Department (sponsor institution) to the American Association of Comparative
Law. She is also the scientific director of the Master in International Trade Law,
co-organised with ITC-ILO, in cooperation with Unicitral and Unidroit. Cristina
is a graduate of the University of Turin (MA) and Florence (PhD). In her career,
she has been a Marie Curie IEF Fellow (Université Panthéon-Assas) and a Max
Weber Fellow (EUI).
Luigi Portinale is full Professor of Computer Science and Artificial Intelligence at
Università del Piemonte Orientale (UPO) in Italy. He has been working on AI for
more than 30 years dealing with several topics: knowledge representation, uncer-
tain and probabilistic reasoning, case-based reasoning, machine learning and
deep learning. He is currently the director of the Research Center on Artificial
Intelligence at UPO (AI@UPO). He has published one book, more than 150 papers
on international journal and referred conference proceedings, and edited several
volumes. He is member of the Italian Association for Artificial Intelligence (AI*IA)
and of the Association for the Advancement of Artificial Intelligence (AAAI,
formerly American Association for AI).
Piercarlo Rossi is full Professor of Private Law, International Contract Law and
Comparative Legal Systems at the Department of Management of the University
of Turin. From 2019 he has been appointed as President of The University Institute
of European Studies (IUSE) in Turin. He is a member of the steering committee of
several publications such as the Springer series ‘Law, Governance and Technology’.
Prof. Rossi is scientific director and coordinator of numerous national and
European projects. His research interests are mainly focused on the comparison of
legal reforms occurring in Europe and Asia, law and economics, law and informa-
tion technology.
Karin Sein is Professor of Civil Law and the Deputy Head of the Institute of
Private Law in the Faculty of Law of the University of Tartu, Estonia. Her main
xx Notes on Contributors

research interests cover domestic and European contract law, consumer law,
private international law and international civil procedure and also law and
digitalisation. Since 2018, she has been leading the 4-year scientific project funded
by the Estonian Research Council and concentrating on consumer contract law
in the Digital Single Market. In recent years, she has provided expertise for the
Estonian Ministry of Justice on implementing European consumer protection
directives into Estonian contract law. During the Estonian EU Presidency in
July–December 2017, she was acting as Chair for the Council Working Group for
the Proposals of the Directive on Digital Content and of the Directive on Online
Sale of Consumer Goods.
Arvi Tavast is Director of the Institute of the Estonian Language. Arvi holds a PhD
from the University of Tartu (2008). Dr Tavast has been involved in development
of dictionaries and language models in public and private sector (eg, acted as the
developer for Ekilex). Arvi has numerous publications on language technology,
copyright and personal data. He has also supervised several theses.
Mimi Zou is Associate Professor (Reader level) at the School of Law, University
of Reading, UK. She was formerly the Principal Investigator of the Deep Tech
Dispute Resolution Lab at the University of Oxford. Mimi’s research interests are
in comparative contract and commercial law and the intersection between law and
technology, especially in the area of dispute resolution.
 1
Mapping Artificial Intelligence:
Perspectives from Computer Science

LUIGI PORTINALE

I. Introduction
Artificial Intelligence (AI) is a mature discipline stemming from computer
science, now pervading every discipline (scientific and non-scientific) and several
aspects of our everyday life. Even if understanding the design and implementation
of intelligent systems requires specific skills in computer science, mathematics
and statistics, gaining awareness about AI and the impact of related technolo-
gies is now becoming a must in humanities as well, and law is not an exception.
As shown in Figure 1 below, AI is at the intersection between several subfields
of mathematics (in particular, logic, probability theory, statistics, optimisation,
calculus, linear algebra) and computer science (theory and practice of algorithms
and data structures, complexity theory, programming languages, computational
architectures).

Figure 1 The AI landscape

Moreover, a specific subfield of AI (exploiting substantial elements of both math-

ematics and computer science) is the Machine Learning (ML) field; the goal of ML
4 Luigi Portinale

is to provide artificial systems (let’s call them agents) with learning from experi-
ence capabilities. Even if very often ML is confused with AI ‘in toto’, it should be
clear that it is just a subfield with specific goals concerning automatic learning from
data and situations, while AI has a wider objective concerning the construction of
an intelligent agent that, once it has learned the needed knowledge (either from
data or because externally provided in some other form) exploits such a knowl-
edge to perform a specific task, that we usually consider to require some form of
intelligence to be solved. For example, old fashioned expert systems1 were usually
built without any learning component; the knowledge base needed to perform the
target task (eg, the suggestion of a therapy, the discovery of a mineral deposit, the
design of a computer configuration just to cite some real-world applications that
have been tackled by this approach) was usually constructed through a manual
process called knowledge engineering, where the computer scientist had to work
in strict contact with a ‘domain expert’, with the goal of transferring some of the
expert’s knowledge into the system and in the suitable formalism. This approach
quickly showed its limits, highlighting more and more the need for automatic
learning as promised by ML approaches (discussed further below).
On the other hand, while evaluating the impact and the influence with respect
to other disciplines, we can notice that, in addition to computer science and
mathematics, AI plays an important role, and is somehow influenced, by several
other disciplines, some scientific (as in the case of biology and neuroscience),
some more related to humanities (as in the case of philosophy, sociology and law)
and some at the border between the two (cognitive science and psychology). It
is worth noting that the impact mentioned above is, however, bidirectional: AI
is definitely bringing new opportunities and interesting applications in all such
fields, while at the same time it can take insights and principles from them. For
instance, cognitive science has been the inspiration for the so-called Cased-Based
Reasoning (CBR) paradigm, one of the most popular reasoning frameworks in
the application of AI in law. The idea is to solve new problems, or to interpret new
situations, by resorting to the solution or the interpretation of similar problems
already solved (or interpreted) in the past. This methodology for problem solving,
that avoids reasoning from scratch every time a new problem in encountered, is a
typical cognitive scheme of humans that can be successfully transferred to artificial
agents. Another source of inspiration comes from neuroscience: our brain, which
is the ‘tool’ by which we, as humans, perform our reasoning patterns, is composed
of several (billions) of interconnected cells called neurons; each connection is
called a synapse, and the information needed to perform our reasoning and activi-
ties flow from neuron to neuron through the synapses in the form of electrical
signals. Even if we do not really know how this process actually works in detail,
an ultra-simplified version of the brain, the so-called neural network model, is one
of the most successful methodologies in modern AI, and it forms the basis of the

1P Jackson, Introduction to Expert Systems 3rd edn (Addison Wesley, 1998).

 Mapping Artificial Intelligence: Perspectives from Computer Science 5

ML approach called Deep Learning. What is really impressive is that, even if an

artificial neuron has actually nothing to do with a natural neuron, the simplified
reconstruction of the whole architecture of the brain, as done in an artificial neural
network, seems to be able to mimic some important tasks like object classification,
image recognition, interpretation of language sentences, and so on. And all of that
is in essence only a matter of ‘matrix manipulation’, in other words a series of sums
and multiplications of real numbers (ie, entities that a computer can handle easily,
in a much more efficient way than a human being can).
The path to the current stage of AI started in 1956, when a group of researchers
headed by John McCarthy organised the Dartmouth Summer Research Project on
Artificial Intelligence, which is now considered the official birthplace of the disci-
pline, and where the term ‘Artificial Intelligence’ has been coined.2 The initial steps
of AI were essentially based on the exciting possibility of building programs able
to exhibit intelligent behaviour at the human level. However, the initial optimistic
predictions about such a possibility immediately faced the hard truth: under-
standing and replicating human intelligence is an extremely difficult problem and
involves the definition of sophisticated methodologies, as well as the availability
of suitable computational resources. The history of AI has thus evolved between
ups and downs in the so-called ‘AI seasons’, where spring days (with a lot of fund-
ing and active projects) were followed by winter days (where funding almost
completely disappeared, and the disappointment for the results was very high).
Today, we are experiencing a very sunny summer, with a renewed interest
for AI methods and applications never seen before. This is due essentially to the
availability of three main ‘assets’: new methodologies for AI model building and
refinement (from basic research in computer science and related disciplines), a
huge amount of available data of very different types (text, images, sounds, struc-
tured data, etc …), and finally large scale and high-performance computational
resources. The rise of the so-called deep learning approach3 allows us to address
very difficult problems requiring complex reasoning capabilities as in decision
support, reactive behaviour as in autonomous devices, interactive behaviour as in
personal assistant devices understanding natural language, and so forth.
The impact of such methodologies pushes the current technologies towards
several new applications, by producing complex side-effects on the everyday envi-
ronment, in particular at the socio-economical, juridical and ethical level. In the
present chapter, we will review the mainstream approaches that have been devel-
oped in AI during the years, until the current rise of deep learning. We will then
discuss the current trends, their strengths and their limitations and the impact on
the socio-economical system related to the adoption of more and more sophisti-
cated intelligent devices and tools.

2 J Moor, ‘ The Dartmouth College Artificial Intelligence Conference: The Next Fifty years’, (2006) 27

AI Magazine 4, 87–90.
3 I J Godfellow, Y Bengio and A Courville, Deep Learning (MIT Press, 2016).
6 Luigi Portinale

II. The AI Seasons

Since the very beginning, AI has raised a very fundamental question: what do
we expect from an artificial system for it to be defined as ‘intelligent’? In fact, as
noticed by many researchers, the problem with AI is in the name itself: while we
are pretty confident in the precise meaning of the word ‘artificial’ (ie, something
that has been built by a human being), we do not have any precise definition of the
term ‘intelligence’. And this is because we associate so many facets with the human
intelligence, that it becomes really hard to be able to condensate all of them in a
single definition.
Returning to the original question, it is clear that it concerns the actual goal
of AI and given that, we have seen two main schools of thought take hold: strong
AI and weak AI. According to strong AI, the computer is not merely a tool in the
study of the mind; rather, as stated by John Searle: ‘The appropriately programmed
computer really is a mind in the sense that computers given the right programs can
be literally said to understand and have other cognitive states.’4 To demonstrate the
impossibility for computer programs to achieve this level of cognition (including
to have consciousness), Searle imagined a mental experiment called the Chinese
Room. Consider a computer program that takes as input a sequence of Chinese
characters and, through a set of specific, and perhaps very complex rules, it is able
to produce other Chinese characters as output. The program is so sophisticated so
that it can pass the so called ‘Turing test’ (it may be confused with a human being,
if involved in a conversation with another human being).5 The argument raised by
Searle is that the computer does not really understand what it is doing. To sustain
that, he considers himself being provided with an English version of the program
instructions; if he receives a sequence of Chinese characters, he will be able to
produce another sequence of Chinese characters by following the instructions,
and by simulating a Chinese conversation. However, he ‘does not speak a single
word of Chinese’ and so he is does not understand Chinese; in the same way, the
program is also not able to understand the Chinese language.
Strong AI has not been seriously considered and investigated inside computer
science, since more emphasis has been given to practical aspects concerning the
construction of systems able to exhibit behaviour that may be considered intel-
ligent by commonsense. This is exactly the issue raised by the weak AI hypothesis:
the goal is to build programs (software) that may exhibit intelligent behaviour in
restricted and well-specified tasks. A computer program able to diagnose specific
diseases in a particular field of medicine can be considered intelligent (by weak AI
point of view), even if the same program is not able to understand a sentence in
any natural language, or is not able to recognise any kind of physical objects, or is
not able to play a simple game like tic-tac-toe.

4 JR Searle, ‘Minds, Brains and Programs’ (1980) 3(3) Behavioral and Brain Sciences 417–457.
5 AM Turing, ‘Computing Machinery and Intelligence’ (1950) 59 Mind 433–460.
 Mapping Artificial Intelligence: Perspectives from Computer Science 7

Moreover, a clear feature of what we consider intelligent behaviour is the ability

to learn from experience. This has given birth to an important subfield of AI which
is ML. However, the construction of (weak) intelligent systems and the investiga-
tion of learning capabilities have usually followed quite separate paths, with several
traditional AI systems (we can call them knowledge-based systems) built without a
learning component, or with a learning component that was not tightly integrated
into the system itself but used as a quite independent module. The equivalence that
we can see today in several documents and papers between AI and ML is then not
properly justified, both because AI is a more general term than ML, and because of
the historical reasons mentioned above.
Regarding the first years of AI, they have been characterised by what John
McCarty called the ‘look Ma, no hands’ period. The time featured great optimism
and very high expectations on what AI could be able to achieve. Some of the most
prominent figures of the time like Herbert Simon (Nobel prize for economics in
1978, and the Turing award in 1975) and Marvin Minsky (Turing award in 1969)
were very confident in producing the following predictions:
• ‘Machines will be capable, within twenty years, of doing any work a man can
do.’ (H Simon, 1965).
• ‘Within a generation … the problem of creating artificial intelligence will
substantially be solved.’ (M Minsky, 1967).
• ‘In from three to eight years we will have a machine with the general intelli-
gence of an average human being.’ (M Minsky, 1970).
None of these expectations were actually met, since the problems related to making
AI successful were much harder to be tackled than imagined at first.
AI started a cycle of seasons, alternating disappointments and failures with
enthusiasm and successes. The first AI winter started in the 1970s, when it became
clear that building systems able to exploit common-sense knowledge was actu-
ally very difficult, and when the limits of logic-based approaches (the dominant
approaches at the time) became evident (in particular in dealing with situations
involving uncertain knowledge and reasoning under uncertainty). Moreover, a
discovery (that went down in history as the Moravec’s paradox) especially impacted
the field: contrary to some traditional assumptions, basic sensory processing and
perception seem to require significantly more computational resources than
modelling high-level reasoning processes. In other words, it is easier to build a
system able to play an intelligent game like chess at the human-champion level,
than to build a system having the same sensory capabilities of a two-year-old baby.
The recognition of such limits had the consequence of reducing the scope of AI
methodologies, and this was actually a good choice, since it focused more atten-
tion on the more practical (and somewhat easier) weak AI hypothesis. Indeed,
the 1980s became what has been called the first AI spring. Research in AI focused
on intelligent architecture called expert systems; they were systems able to exhibit
competence at the human expert level, but only in very restricted areas like the
8 Luigi Portinale

diagnosis of restricted classes of diseases, suggestion of specific antibiotic thera-

pies, discovery of mineral deposits, determination of the optimal customer specific
configuration of a computer, and so forth. In practice, the victory of weak AI
against strong AI. Expert systems like MYCIN,6 CADUCEUS,7 PROSPECTOR,8
and R19 became the most important representatives of the so-called rule-based
systems, that is systems with a reasoning paradigm consisting in formalising the
problem solving knowledge of a specific problem or task in a set of if-then rules;
such rules allows the system to test the occurrence of a specific condition (the
if part), and if this condition is found to occur, the system is allowed to obtain
a conclusion (the then part). The idea was to have a mechanistic and possibly
efficient way of simulating a form of deductive reasoning (ie, from premises to
intermediate and then final conclusions). In the same period in Japan, the so
called Fifth Generation Computer project was started, with the aim of using
PROLOG-based logic programming as the main tool for building AI systems.
PROLOG is a programming language born in Europe10 aimed at implement-
ing the type of rule-based reasoning we mentioned above, but with more formal
semantics based on a specific (restricted) type of logical reasoning: resolution
with Horn clauses.11 At that time, the main programming language used to build
knowledge-based systems was LISP,12 a language proposed by John McCarthy that
quickly became the standard (especially in the US) in the expert system indus-
try. Some computer manufacturing companies even started the construction and
the marketing of ad-hoc computer architectures called Lisp Machines. They were
actually general-purpose computers, but designed to efficiently run Lisp as their
main software and programming language, usually via specific hardware support.
LISP was one of the first functional languages (influenced by the Alonzo Church’s
lambda calculus13) and its list manipulation primitives (the name is the acronym
of the LISP Processor) made it useful to the symbolic manipulation operations
required by the AI systems of that time. The Fifth Generation Computer project
was among other things the Japanese answer to such a business operation driven

6 BG Buchanan and EH Shortliffe, Rule Based Expert Systems: The MYCIN Experiments of the

Stanford Heuristic Programming Project (Addison-Wesley, 1984).

7 G Banks, ‘Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach’

(1986) 1(1) Critical Reviews in Medical Informatics 23–54.

8 AN Campbell, VF Hollister, RO Duda and PE Hart, ‘Recognition of a Hidden Mineral Deposit by

an Artificial Intelligence Program’ (1982) 217 Science 927–929.

9 J McDermott, R1: an Expert in the Computer Systems Domain, Proc. First National Conference on

Artificial Intelligence (AAAI 80), (AAAI Press, 1980) 269–271.

10 A Colmerauer and P Roussel, ‘ The birth of Prolog’ (1993), ACM Special Interest Group on

Programming Languages (SIGPLAN) Notices. 28(3) 37–52.

11 JA Robinson, ‘A Machine-Oriented Logic Based on the Resolution Principle’ (1965) 12(1) Journal

of the Association for Computing Machinery 23–41.

12 J McCarthy, R Brayton, D Edwards, P Fox, L Hodes, D Luckham, K Maling, D Park and S Russell,

LISP I Programmers Manual (MIT Press, 1962): Artificial Intelligence Group, M.I.T. Computation
Center and Research Laboratory (http://history.siam.org/sup/Fox_1960_LISP.pdf ).
13 The Lambda Calculus, introduced by American mathematician Alonzo Church in the 1930s, is a

formal system in mathematical logic for expressing computation based on function abstraction.
 Mapping Artificial Intelligence: Perspectives from Computer Science 9

by American companies. Despite the failure of the original Japanese project,

PROLOG is still adopted in several AI systems, especially with extensions that
have been provided in modern releases, that allows to efficiently address combi-
natorial optimisation tasks such as timetabling, resource allocations, planning and
scheduling.
Finally, by the end of the decade, the re-discovery of the backpropagation
algorithm14 for learning the parameters of a neural network was the key for the
resurgence of the so-called connectionism or sub-symbolic approach to AI, ie, the
use of models based on the processing of non-symbolic information, and based on
a metaphor of the connections between neurons in the human brain.
However, the expert system approach quickly showed its limits by producing
another AI winter lasting until the mid-1990s. The main unresolved issue was the
‘knowledge acquisition bottleneck’. In fact, the main source for making an expert
system to work is the knowledge base, a repository of specific domain dependent
knowledge used by the system for the fulfilment of its task. The content of the
knowledge base had to be elicited manually, by a collaborative activity between
the domain expert (the expert in the domain addressed by the expert system) and the
knowledge engineer (the scientist expert in AI methods, but totally unaware of the
domain of interest). This phase proved to be the hardest one in the development
of an expert system, and together with the lack of practical ML methods and tools
for automatic learning of the needed knowledge, the difficulties in the knowledge
acquisition process marked the end of the expert system era. Even connectionist
models, that could exploit raw data more directly in learning the system param-
eters, were rapidly challenged by the complexity of real-world problems, and their
limited learning capabilities were not able to offer practical solutions. The market
of LISP machines started to collapse, and several computer companies that had
performed relevant investments in the expert system business had to quickly
change direction, returning to more traditional kind of applications.
New interests in AI methodologies (and a consequent new AI spring) started
in the 1990s, when probabilistic methods came to the rescue. An important mile-
stone was the publication of Judea Pearl’s book15 advocating the use of probability
theory to deal with both modeling and inference in intelligent systems. Probability
had been rejected by some of the fathers of AI (John McCarthy and Patrick Hayes)
as ‘epistemologically and computationally inadequate’ for AI.16 Pearl disputed this
view, by showing that a consistent interpretation of probability theory is possible

14 The backpropagation algorithm was originally proposed in the context of control theory in the

1960s; its adoption in the Machine Learning setting is generally ascribed to David Rumelhart, Geoffrey
Hinton and Ronald Williams who showed how to efficiently learn the parameters of an artificial neural
network through its use in such a kind of model (DE Rumelhart, GE Hinton and RJ Williams, ‘Learning
Representations by back-propagating Errors’ (1986) 323(6088) Nature 533–536).
15 J Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan

Kaufmann Publ, 1988).

16 J McCarthy and P Hayes. ‘Some Philosophical Problems from the Standpoint of Artificial

Intelligence’, in B Meltzer and D Michie (eds), Machine Intelligence 4 (Edinburgh University Press,
1967) 463–502.
10 Luigi Portinale

by means of graph-based formalism called Probabilistic Graphical Models,17 whose

main representative are Bayesian Networks.18 The use of such formalisms for
compact and efficient modeling of uncertain knowledge, the use of specialised
inference algorithms to answer any kind of probabilistic query, and the discovery
and application of algorithms able to learn both the structure and the parameters of
a graphical model, made it possible to overcome several limitations of logic-based
systems and opened the way to more real-world applications. Evidence of that is
the fact that at the beginning of the new millennium, a company like Microsoft
hired some of the most prominent researchers in the field of Bayesian Networks
(in particular, David Heckerman, Jack Breese and Eric Horvitz) to open a new
research division on AI and ML. That team invented some of the most success-
ful AI applications of the period including the world’s first machine-learning
spam filter, the Answer Wizard (which became the backend for Clippy, the small
avatar helping users during their activity in using the Microsoft Office suite), the
Windows Printer Trouble-shooters and Microsoft’s first machine-learning plat-
form, now represented by Azure. They also released one of the first graphical
tools for building and reasoning with Bayesian Networks, the MSBN (MicroSoft
Bayesian Network) tool. Probabilistic Graphical Models and Bayesian networks
methodologies started to be incorporated in several systems by several companies,
even if the fall of all the previous expectations promised during the expert system
era put a stop to the advertisement of such new services as AI-based. However, the
rapid success and the positive impact of probabilistic-based techniques became
the new instrument for the resurgence of the interest on intelligent systems and
AI in general. At the same time, ML became more and more able to tackle big
problems with the introduction of several statistical methods, such as the Support
Vector Machines (SVM),19 and the Ensemble Learning approaches.20 Ensemble
approaches implement the so called ‘wisdom of the crowd’ idea: if a given ‘process’
is able to perform a given prediction with a particular performance, then taking
into account several processes performing the same prediction can in principle
increase the overall performance in the task. The ‘process’ can be differentiated
by either using different algorithms or using different data. For example, one can
execute a set of different algorithms, producing different prediction models, on
the same set of data, then output the prediction provided by the majority of the
models, possibly by weighting the vote with the corresponding confidence in the
prediction, in such a way that models predicting a result with high confidence
have a greater weight in producing the final answer.21 On the contrary, a different

17 D Koller and N Friedman, Probabilistic Graphical Models (MIT Press, 2009).

18 Charniak, ‘Bayesian networks without tears: making Bayesian networks more accessible to the
probabilistically unsophisticated’ (1991) 12(4) AI Magazine 50–63.
19 C Cortes and V Vapnik, ‘Support-vector Network’ (1995) 20(3) Machine Learning 273–297.
20 L Rokach, ‘Ensemble-based Classifiers’ (2010) 33(1-2) Artificial Intelligence Review1–39.
21 Differently from the basic rule of a democratic ‘real-world’, in the ‘machine world’, a standard

democratic approach where the vote/opinion of each subject (ie model) counts as the vote/opinion of
any other subject is usually not a useful and practical idea. Subjects (ie models or algorithms) perform-
ing better and having more competence have the right to count more.
 Mapping Artificial Intelligence: Perspectives from Computer Science 11

ensemble approach could be to use the same algorithm or model several times, but
on a different set of data. This kind of approach is strictly related to some compu-
tational statistics methodologies, such as the well-known bootstrap method,22 that
represent an effective way for improving the final performance of a predictive
model. The performance is usually measured by means of the accuracy metric,
meaning the percentage of correct predictions over the whole set of prediction
provided; having different opinions over the same set of data or the same opinion
over different chunks of similar data is the ensemble learning way to increase the
final accuracy.
This new spring season had the merit of involving into AI and ML researchers
and practitioners from several different fields and has eventually evolved into a
summer about 10 years ago; this started a totally new era dominated by the exploi-
tation of big data and witnessed an increasing number of successful results in very
different areas. The next section will discuss in more detail.

III. Big Data and Deep Learning

In 2010, The Economist was published with a significant title on the cover: The data
deluge. The cover reported a man carrying a reversed umbrella under a deluge of
data. The reverse umbrella captured part of such data, and a plant was watered
through the collected rain. The big data era was starting, and AI became a way to
exploit such a huge quantity of data made available by both electronic devices as
well as human activities.
Availability of big data is, however, only one source of the current success of AI
methods; having a huge amount of data available can be of no use unless analyti-
cal methods are available to extract useful information (we may say knowledge)
from it. And such methods should be able to provide answers in a reasonable
time. Fortunately, researchers in computer science and engineering have been able
to provide both new analytical methodologies and significant improvements in
computational resources. This means that difficult problems requiring a lot of data,
can be solved by resorting to specific and dedicated formalisms and analysed by
exploiting computational facilities (like multi-core CPUs or GPUs) which are by
now far more performant than those available just a few years ago.
Concerning modelling issues, old-fashioned neural networks have been
extended to models now called deep neural networks, giving birth to a novel set
of approaches called Deep Learning. We may say that deep learning methodolo-
gies represent a specific subset of ML, in which models based on neural network
architectures are extended in such a way that dozens or even hundreds of different
layers of neurons can be dealt with. Before the deep learning era, any effort to learn
the parameters of networks with more than a few hidden layers was prohibitive

22 B Efron, ‘Bootstrap methods: Another look at the jackknife’ (1979) 7(1) The Annals of Statistics1–26.
12 Luigi Portinale

and attempts to do so were doomed to fail. Nowadays, the introduction of specific

methods to deal with some learning problems (like the vanishing or explod-
ing gradient problems that cause the learning algorithm to stop learning from
data), together with the availability of very performant computational resources
(in particular, new graphical processing units or GPU, originally thought for
image processing, and now used for general computation as well) allow for the
construction and learning of very deep models.
Moreover, deep learning allows one to address another important issue related
to any ML approach: feature extraction. Before an ML model can be used, the
relevant features of the problem to be solved must be extracted from the available
data. For example, if we want to predict the severity level of a specific disease, we
must obtain knowledge like the age and the gender of the patient, as well as the
symptoms, the results from blood and lab tests and other related features. The set
of attributes relevant to the problem are usually manually extracted from the data
before building the ML model, meaning that a specific task concerning feature
engineering should be put in charge of the analyst. On the contrary, the hidden
layers of a deep network can automatically extract the relevant features from raw
data. An example is given by the CNN (Convolutional Neural Network) model
used for image interpretation. Given a set of pixels representing the image, each
layer is able to abstract specific features of the image such as edges and more and
more specific subparts of the figure, until the network can recognise what is in the
input image. There is no need for partitioning the original image into subparts,
since this is done by the network itself. A related concept is that of feature embed-
dings; this means finding a suitable numeric representation of the objects (images,
sentences or any other kind of signals) that needs to be dealt with. Since every
object is eventually represented as a vector of numbers, numerical operations,
which computers are very comfortable with, can be performed to implement very
complex tasks like object detection, image classification and segmentation, inter-
pretation of sentences in natural language, complex forms of information retrieval
and so forth. For instance, the success of modern chatbots (intelligent artificial
agents able of holding natural language conversation with humans) is essentially
due to the fact that thanks to the word embedding process,23 clusters of similar
words and relationships between words are formed; these relationships are then
exploited to provide a specific role to the words in the sentence and to determine
the meaning of the sentence itself.
The great interest in the deep learning approach is indeed justified by the good
results obtained in several areas; in particular, deep learning is best performing in
the following tasks: image recognition (classification, object detection), language
interpretation (written or spoken), intelligent games (chess, go), autonomous
agents (self-driving cars, space rovers, robots) and precision medicine (CT scans
or MRI interpretation, system biology). Very often, what is important for the

23 D Jurafsky and HM James, Speech and language processing: an introduction to natural language

processing, computational linguistics, and speech recognition (Prentice Hall, 2000) ch 6.

 Mapping Artificial Intelligence: Perspectives from Computer Science 13

above tasks is the final output; for instance, given a CT scan of the lungs of a
patient, the goal is to select the region were a potential tumour is identified. In
such cases, the final output is the desired answer. However, there are situations
where a detailed explanation of the final answer is also needed. This is the case in
decision support where, in order to have the system’s suggestion to be reasonably
accepted, an explanation of why this is the answer should also be provided.
Explainable AI (or XAI) is a new buzzword trying to address the issues related
to the construction of a reasonable explanation for a system answer. As in the case
of deep learning (where the basic neural network models were already available
several years before), for XAI the problems and the potential solutions are also
not new. Again, as in the case of deep learning where the emphasis is on neural
networks models which are well-known models in AI for several years, the explain-
able AI concept is also not new to the AI community. Indeed, providing suitable
explanations was one of the main tasks required by an expert system; since such
systems were usually based on a set of rules to be applied to the available data, the
chain of the rules activated during the reasoning process was sometimes consid-
ered an explanation for the conclusion. However, extracting a set of rules from a
deep network is not easy and it is not completely clear how to address the prob-
lem of explanation in such a setting. This opens the way for a return to the use of
symbolic formalisms based on some form of logic and ontologies (eg, description
logics24) and directly modelling the relationships between specific entities in the
domain of interest. The combination of symbolic and sub-symbolic approaches is
then becoming of great interest in AI.
Another potential pitfall of deep learning approaches is the possibility of being
‘fooled’ by particular techniques of adversarial machine learning.25 For instance,
given an image classifier with a very god accuracy in determining the objects in the
image, a very small perturbation in the pixels of the image can produce completely
different predictions, even if with the human eye, the image appears unchanged. A
well-known experiment showed that a deep network able to recognise with good
accuracy the image of a panda, was almost sure to recognise it as a gibbon after the
input image was ‘corrupted’ with some disruption to the pixels, even if the result-
ant image looked completely unchanged to any human observer. The ‘corrupted’
image is what is called an adversarial example. Adversarial examples exploit the
way ML algorithms (especially those based on the deep learning paradigm) work
to disrupt the behaviour of the same or other AI algorithms. In the past few years,
adversarial ML has become an active area of research as the role of AI continues to
grow in many of the applications we currently use today. As one can easily suppose,
this is a big issue to consider when working with and designing systems based on
deep learning, since it is not hard to imagine a malicious (or even criminal) use of

24 F Baader, I Horrocks and U Sattler, ‘Description Logics’, in F van Harmelen, V Lifschitz and

B Porter (eds), Handbook of Knowledge Representation (Elsevier, 2007) ch 3.

25 I Goodfellow, P McDaniel and PN Papernot, ‘Making machine learning robust against adversarial

inputs’ (2018) 61(7) Communications of the ACM 56–66.

14 Luigi Portinale

such techniques in different contexts (eg, in a military or defence application or in

a legal setting or even in a now quite common environment such as a self-driving
car mistaking a stop signal for something else).

IV. Artificial Intelligence and Law

Since we have seen that modern AI and ML are now having an impact on several
fields and disciplines, it is then not surprising that the law has also to deal with AI.
According to Harry Surden,26 the connection between AI and law involves ‘the
application of computer and mathematical techniques to make law more under-
standable, manageable, useful, accessible, or predictable’. This is not completely
different from what we expect from AI in other disciplines, and in fact AI and
law have a quite significant history, since similar kinds of requirements were often
proposed as inputs to AI systems.
However, AI is definitely successful in domains and tasks when there are specific
underlying patterns, rules and precise or well-defined answers. This is particularly
true for data driven AI like deep learning approaches; the models can dredge up
and mine useful knowledge from the available data, if there are patterns in it, by
also taking into account the fact that data which are unobserved during train-
ing can be input to the system when it is asked to provide answers. Data driven
AI is only partially successful in areas that are value-laden, judgment-oriented,
abstract, and that involve persuasion and argumentation. In this case knowledge-
based systems, where data are integrated with additional knowledge in forms of
semantic networks, ontologies and knowledge graphs can play a significant role.
By looking then at the main applications that we may devise for AI in law, it is quite
clear that this integration is of paramount importance.
An example of such a kind of integration is provided by the Case-Based
Reasoning (CBR) paradigm,27 where lazy learning methods are complemented
with specific knowledge sources. A lazy learning methodology learns to solve
specific cases by storing all the past cases already solved by the system, together
with the corresponding solution. When a new case must be solved, the system
retrieves the set of cases most similar to the current one from its memory, and uses
the retrieved solutions as the basis for the solution of the current target case.28 This
kind of precedent-based reasoning is similar to the pattern that is followed in judi-
cial systems that are committed to precedents to obtain a sentence; indeed, a lot
of research has been conducted in the US concerning the application of the CBR

26 H Surden, ‘Artificial Intelligence and Law: An Overview’ (2019) 35 Georgia State University Law

Review, available at: ssrn.com/abstract=3411869.

27 MM Richter and RO Weber, Case Based Reasoning: a textbook (Springer, 2013).
28 A Aamodt and E Plaza, ‘Case-Based Reasoning Foundational Issues, Methodological Variations,

and System Approaches’, AI Communications. (IOS Press, 1994)39–59.

 Mapping Artificial Intelligence: Perspectives from Computer Science 15

methodology in law practice since the very beginning.29 However, a complete

CBR system cannot rely only on past data and precedents, but also needs specific
knowledge, in order to build the solution to the target case from the old ones.
Ontologies, as well rules and knowledge graphs can be usefully adopted to this
end.30 Despite that, the realisation of this important part of a CBR system (called
the revise or adaptation step) may result in the implementation of a complete
knowledge-based system. In a legal domain, this has often implied that a flex-
ible interleaving between case-based and rule-based reasoning was a potential
solution.31 In fact, as reported by Quattrocolo,32 it is impossible to translate all
norms and acts into mathematical, computational rules, thus the case-based
model seems to be a better option; but since the basic difference in the value of the
precedent in common law and in civil law is very relevant, the suitable combina-
tion of cases with rules and other structured forms of knowledge is a key success
to the introduction of AI methodologies in this setting.
A further aspect related to the use of AI in law concerns the problem of
performing judgment under uncertainty. As we have previously mentioned, one
of the reasons for the failure of purely logic-based systems in the first period of AI
was the unsuitability of such formalisms to properly deal with uncertainty issues.
This caused the rising of the probabilistic revolution headed by the introduction of
Bayesian networks and the resurgence of interest in subjectivist or Bayesian forms
of reasoning under uncertainty.33
This means that, when reasoning under uncertain conditions, and with the
goal of providing a suggestion or a decision, an intelligent system must exploit
any relevant knowledge it has, in addition to the available data; the use of prior
knowledge must be incorporated into the reasoning process, exactly as is done
in Bayesian statistics. The well-known prosecutor fallacy34 is an example where a
correctly designed AI system can provide the right answers contrary to the erro-
neous common-sense conclusions reached by several (even potentially expert)
humans.
Suppose that a positive DNA match has been found for a given suspect (let us
call him Fred) on the crime scene. Scientific evidence suggests that the probability

29 KD Ashley, ‘Case-Based Reasoning and its Implications for Legal Expert Systems’ (1992) 1 Artificial

Intelligence and Law 1113–208.

30 A Wyner, ‘An Ontology in OWL for Legal Case-Based Reasoning’ (2008) 16 Artificial Intelligence

and Law 361–387.

31 EL Rissland and DB Skalak, ‘Combining case-based and rule-based reasoning: a heuristic approach’

(1989), Proceedings of the 11th international Joint Conference on Artificial intelligence (IJCAI 89)
(Vol. 1), Detroit (MI), 524–530.
32 S Quattrocolo, Artificial Intelligence, Computational Modeling and Criminal Proceedings: a frame-

work for a European legal discussion (Springer, 2020).

33 It is worth noting that, despite the word ‘Bayesian’ in their names, Bayesian networks can be inter-

preted as frequentist models as well, since their definition does not rely on any specific interpretation
of the concept of probability.
34 WC Thompson and EL Shumann, ‘Interpretation of Statistical Evidence in Criminal Trials: The

Prosecutor’s Fallacy and the Defense Attorney’s Fallacy’ (1987) 2(3) Law and Human Behavior 167–187.
16 Luigi Portinale

of having that kind of DNA for a random subject is very low, say 1 in over 1,000
people. Since Fred has a positive match, the prosecutor’s argument is that Fred
must be guilty, since there is a very small probability that the match is positive
by chance. The argument is a kind of common-sense mistake that several people
would make; in particular, the source of the problem is that the estimate of a prob-
ability of innocence equal to 0.1 per cent actually refers to the probability of getting
a positive match, given that Fred is innocent. This is because, given the fact that
Fred is innocent, then there is a 0.1 per cent probability that Fred gets a positive
DNA match (as stated before this is the probability of getting a positive match by
chance). The actual estimate that the prosecutor should consider is instead the
probability of Fred being innocent, given that the DNA match is positive. This is
easily computable using the well-known Bayes formula, by also providing the
prior probability (estimated before collecting any evidence about the DNA) that
Fred is innocent (alternatively guilty). For instance, if we have no specific reason
to suspect Fred is guilty, if the crime has been committed in a community of 1,000
people, we can easily compute the probability of Fred being innocent, given the
positive DNA match, as about 91 per cent. This would completely demolish the
prosecutorial framework based on a clear fallacy in the reasoning process. An
AI agent correctly implementing this sort of Bayesian reasoning under uncer-
tainty would not be prone to similar fallacies. Moreover, the impact of the prior
knowledge is something that is very often either neglected or overestimated in
common-sense reasoning by people. If Fred is a good citizen with no precedents,
and this is the situation for almost all the people in the community of reference,
then associating a uniform prior distribution to get the prior probability of Fred
being guilty can be reasonable; however, if we know a history of precedents for
similar crimes committed by Fred, then this prior probability should adequately
reflect this situation. The problem of eliciting the right priors in a specific problem
is still considered a field of active research, but some methods can be success-
fully exploited in several practical situations, leading to the design of AI systems
that can avoid mistakes such as that discussed above. It is also worth mentioning
that this is not an abstract or ‘academic’ problem, since there have been real cases
where this kind of fallacy has unfortunately been reported, and in particular the
Sally Clark case35 in a UK court, and the Lucia De Berk case36 in a Dutch court. In
the first case, where Sally Clark was accused of the murder of her two infant sons,
some simple considerations of the principles underlying probabilistic graphical
models (and Bayesian networks in particular) could have avoided several wrong
conclusions based on the wrong set of assumptions, such as the failure to consider
a common cause in the sudden death of the infants.
Finally, another important issue that comes with purely data driven AI is the
problem of fairness; a given algorithm is said to be fair, or to have fairness, if its

35 CJ Bacon, ‘The Case of Sally Clark’ (2003) 96(3) Journal of the Royal Society of Medicine 105.
36 RD Gill, P Groeneboom and P de Jong, (2019) ‘Elementary statistics on trial (the case of Lucia de
Berk)’, Arxiv, arxiv.org/abs/1009.0802.
 Mapping Artificial Intelligence: Perspectives from Computer Science 17

results do not depend on some specific and given variables, particularly those
considered sensitive, such as the traits of individuals which should not correlate
with the outcome (ie, gender, ethnicity, sexual orientation, disability, political
preferences, etc …). Machine learning methods that only rely on data can be
in principle biased, that is subject to any bias present in the data itself. This is
especially remarkable in applications of AI to law. For instance, in US courts it is
quite common for assessment AI algorithms designed to consider the details of a
defendant’s profile and returning a recidivism score estimating the likelihood that
he or she will reoffend to be used; if the algorithm has been instructed and has
been given biased data, then such biases will reappear in the score that the algo-
rithm will compute (for instance by making higher the likelihood of recidivism
for specific ethnic communities, as reported by ProPubblica news organisation37).
Law then is an area that can be really challenging for AI and the use of intel-
ligent algorithms in this field should be carefully dealt with, by considering a
suitable classification of AI and law users. In particular, Surden proposes to distin-
guish the needs of different categories of users:
1. administrators of law (judges): they need support for sentencing and bail
decisions;
2. practitioners of law (attorneys): they need to predict legal outcomes (eg, CBR),
predictive-coding (retrieval of relevant document for a litigation), or docu-
ment assembly support (for contract review and negotiation);
3. users governed by law (citizens, companies): they need law compliance check-
ing, legal self-help systems, or computable contracts.
Having in mind these differences would definitely help in focusing on the right
AI methodology, and knowing its strengths and weaknesses would force a correct
application of the methods and models in a proper context.

V. Conclusions
This chapter has presented an historical excursus of AI and ML, by trying to point
out ideas, expectations and both possibilities and limits of the related methodolo-
gies. The viewpoint is that of computer science, the science of which AI and ML
are a part; we have also briefly discussed the social impact of intelligent systems,
by focusing on applications of AI and law. Today we are experiencing a new
industrial revolution guided by AI, and the impact on everyday life begins to be
perceived. New professional profiles with interdisciplinary competences will be
needed to both devise new applications and govern such an important revolution.
Last but not least, ethics and values must be heavily taken into account in this

37 www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing (last accessed

7 Apr 2021).
18 Luigi Portinale

framework, in order to avoid problems concerning safety, transparency, explain-

ability and human values preservation like rights, cultural differences and judicial
fairness. To this end, the Asilomar manifesto for AI principles of the Future of Life
institute38 promotes specific principles that should be satisfied for a beneficial AI.
They are organised in three different areas: research, ethics and values, longer-term
issues. Research issues are related to the goals of AI (to create beneficial AI and
not undirected AI), to the way funding and investments should be accomplished,
to the correct link between science (AI) and policy makers, to the fostering of a
new culture of cooperation and trust, and to the avoidance of corner-cutting on
safety standards. But the emphasis of the manifesto is given to ethics and values
principles that should be promoted and firmly kept when designing and develop-
ing intelligent systems impacting our every-day life. Safety (an AI system must
not harm), failure transparency (we must know the reason of a failure), judicial
transparency (any automatic decision making process should be auditable by a
competent human authority), responsibility of designers and implementers, value
alignment with the human values like dignity, freedom, rights and cultural diver-
sity, personal privacy, liberty (AI applications must not curtail real or perceived
personal liberty), sharing of benefits and economic prosperity among people, human
control of every AI activity or application, non-subversion (the power conferred by
control of highly advanced AI systems should respect and improve, rather than
subvert, the social and civic processes on which the health of society depends),
and finally the avoidance of lethal autonomous weapons through the so-called AI
arms race.
To complete the figure, longer-term issues concern risk assessment and mitiga-
tion, careful management and planning of the impact of AI applications (especially
those proposing a real revolution in human relationships, jobs and activities), the
avoidance of strong assumptions regarding upper limits on future AI capabilities
(the so-called capability caution principle), the presence of strict safety and control
measures related to possible self-improvement capabilities of AI systems, and
finally the common good principle: AI applications and systems should only be
developed in the service of widely shared ethical ideals, and for the benefit of all
humanity rather than one state or organisation.
Taking into account what AI has been, what AI currently is and all such prin-
ciples, we may hope to design and build a future for AI that can get rid us of the
fears usually associated with a science-fiction scenario represented by a society
where humans have lost the control of their activities, or even of their lives. In
this scenario intelligent agents, which are not necessarily robots, but also simple
programs with no physical interaction with the external environment, will cooper-
ate with humans, helping them to solve old and new problems in a more efficient
and comfortable way.

38 futureoflife.org/ai-principles.
 2
Artificial Intelligence, Contracting
and Contract Law: An Introduction

MARTIN EBERS*

I. Introduction
We are witnessing a major revolution in the ways in which contracts are initi-
ated, negotiated, concluded, performed and enforced. One of the most significant
trends in the field of contracting and contract law is the use of Artificial Intelligence
(AI) techniques, such as machine learning (ML) and natural language processing
(NLP) – deployed by many companies during the whole lifecycle of a contract to
make contracting more efficient.
This chapter gives an overview of the use of AI in contracting (II.) and the
manifold challenges that arise from it under contract law – ranging from free-
dom of contract and party autonomy (III.), pre-contractual duties (IV.), formation
of contract (V.), defects in consent (VI.), incorporation of standard terms (VII.),
interpretation (VIII.), to fairness of contracts (IX.), contractual liability (X.) and
the question of whether contract law itself might eventually become superfluous
in the age of AI (XI.).
In order to narrow down the topic, some clarifications are necessary. First, the
purpose of this chapter is not to provide a detailed exposition on the numerous
legal issues that arise under contract law when AI systems are used. The literature
on this topic merits further analysis but such an endeavour would go far beyond
the scope of this chapter. Rather, the focus is on providing an overview of the
challenges that arise under contract law when AI systems are used.
Second, it should be noted that the term ‘artificial intelligence’ is used here in a
narrow sense1 to refer primarily to data-driven technologies such as ML, including
NLP as a subfield of ML which refers to the ability of a computer to understand,

* This work was supported by Estonian Research Council grant no PRG124. All internet sources
were last accessed on 8 March 2022.
1 In contrast, the proposal for an ‘Artificial Intelligence Act’ (hereinafter: AIA) published by the

European Commission in April 2021 uses a much broader definition; European Commission, Proposal
for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act),
COM(2021) 206 final. Art 3(1) AIA in conjunction with Annex I covers almost every computer program,
20 Martin Ebers

analyse, manipulate, and potentially generate human language. Data-driven tech-

nologies are fundamentally different from earlier forms of automation. In the past,
many algorithmic systems, especially expert systems, relied on rule-based condi-
tional logic operations using symbolic rules to represent and infer knowledge. By
contrast, the current wave of successful AI applications is rooted in data-learned
knowledge, which relies less on hand-coded human expertise than the knowledge
learned from data. Instead of programming machines with specific instructions to
accomplish particular tasks, ML algorithms enable computers to learn from the
‘training data’ and experience.
Based on this notion, contracts using AI systems must be distinguished from
smart contracts. The term ‘smart contracts’, which famously can be traced back to
Nick Szabo,2 refers to a special protocol, the distributed ledger technology (DLT),
especially blockchain, intended to contribute, verify or implement the negotiation
or performance of the contract in a trackable and irreversible manner without
the interference of third parties.3 Accordingly, smart contracts are based on a
completely different technology. While it is true that there have been increasing
efforts to merge blockchain and AI in recent years,4 the concept of smart contracts
still very much refers to self-executing promises based on a blockchain rather than
AI-driven contracts.
Finally, the use of AI systems by a company during the ‘life cycle’ of a contract
must be delineated from the constellation in which AI systems themselves are the
subject of the contract.5 The latter case concerns contracts where providers offer
‘AI as a Service’ (AIaaS)6 or AI-based (smart) products/services7 – and the related

since the definition for AI refers not only to machine learning, but also to logic and knowledge-based
approaches, including knowledge representation, inductive (logic) programming, knowledge bases,
inference and deductive engines, (symbolic) reasoning and expert systems. However, such a definition
is overly broad; cf M Ebers et al., ‘The European Commission’s Proposal for an Artificial Intelligence
Act – A Critical Assessment by Members of the Robotics & AI Law Society (RAILS)’ (2021) 4(4)
Multidisciplinary Scientific Journal 589–603. https://doi.org/10.3390/j4040043.
2 According to Nick Szabo, a smart contract is a ‘computerized transaction protocol that executes the

terms of a contract. The general objectives of smart contract design are to satisfy common contractual
conditions (such as: payment terms, liens, confidentiality, and enforcement etc.), minimize exceptions
both malicious and accidental, and minimize the need for trusted intermediaries like banks or other
kind of agents’; N Szabo, ‘Smart Contracts’, www.fon.hum.uva.nl/rob/Courses/InformationInSpeech/
CDROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/smart.contracts.html.
3 For an overview on the different smart contracts definitions, see M Finck, ‘Grundlagen und

Technologie von Smart Contracts’, in M Fries and BP Paal (eds), Smart Contracts (Mohr Siebeck, 2019)
1–12.
4 On the convergence of blockchain and AI ‘to make smart contracts smarter’ see, in this book,

ch 3, under II.C. Moreover, see The European Union Blockchain Observatory & Forum, Convergence
of Blockchain, AI and IoT, v.1.1, 21 April 2020, www.eublockchainforum.eu/sites/default/files/
report_convergence_v1.0.pdf.
5 S Grundmann and P Hacker, ‘Digital Technology as a Challenge to European Contract Law – From

the Existing to the Future Architecture’ (2017) 13(3) European Review of Contract Law 255–293, 264.
6 Typically, AIaaS providers offer their customers access to pre-built AI models and services via APIs

(application programming interfaces). However, usually, AIaaS is offered only to commercial organi-
sations and public sector bodies, and not to consumers. Cf S Parsaeefard et al., ‘Artificial Intelligence
as a Services (AI-aaS) on Software-Defined Infrastructure’ 2019 IEEE Conference on Standards
for Communications and Networking (CSCN), 2019, 1–7, doi:org/10.1109/CSCN.2019.8931372.
Javadi, Cloete, Cobbe, Lee and Singh, ‘Monitoring Misuse for Accountable “Artificial Intelligence as
 Artificial Intelligence, Contracting and Contract Law: An Introduction 21

question as to what requirements should be placed on contractual conformity

when a lack of conformity exists, and under what preconditions the trader is then
liable to the customer.8 This topic will not be explored further. Instead, this chapter
focusses on the internal use of AI systems to initiate, negotiate, conclude, perform
or enforce contracts and its manifold implications on contract law.

II. Contracting in the Age of AI

AI techniques, such as ML and NLP, are used by many companies during the entire
‘lifecycle’ of a contract to make contracting more efficient.
At the pre-contractual stage, AI-driven profiling techniques provide better
insights into customers’ behaviour, preferences, and vulnerabilities. Companies
can not only tailor their advertising campaigns9 but also their products and
prices10 specifically to suit the customer profile, credit institutions can use the
profiles for credit ratings,11 and insurance companies can better assess the insured
risk.12 In particular, AI-driven big-data profiling techniques give companies the
opportunity to gain a more profound understanding about customers’ personal
circumstances, behavioural patterns, and personality, including future prefer-
ences. These insights enable companies to not only tailor their advertisements
(so called ‘online behavioural advertising’)13 but also their contracts14 in ways that
maximise their expected utility.

a Service”’ (AIES 2020: AAAI/ACM Conference on AI, Ethics and Society, New York, 7–8 February
2020); M Berberich and A Conrad, ‘§ 30 Plattformen und KI’ in M Ebers et al. (eds), Künstliche
Intelligenz und Robotik – Rechtshandbuch (CH Beck 2020) 930ff, 938ff.
7 Eg self-driving cars, vacuum cleaners, surveillance equipment, health apps, voice assistants, and

translation apps.
8 Cf thereto (for consumer contracts) M Ebers, ‘Liability for Artificial Intelligence and EU Consumer

Law’(2021) 12 Information Technology and Electronic Commerce Law (JIPITEC) 204–221, 62 ff,
www.jipitec.eu/issues/jipitec-12-2-2021/5289.
9 Cf R Calo, ‘Digital Market Manipulation’ (2014) 82 The George Washington Law Review 995,

1015ff, dx.doi.org/10.2139/ssrn.2309703; N Helberger, ‘Profiling and Targeting Consumers in

the Internet of Things – A New Challenge for Consumer Law’ in R Schulze and D Staudenmayer
(eds), Digital Revolution: Challenges for Contract Law in Practice (Nomos, 2016) 135–164, doi.
org/10.5771/9783845273488.
10 FZ Borgesius and J Poort, ‘Online Price Discrimination and EU Data Privacy Law’ (2017) 40

Journal of Consumer Policy 347–366, doi.org/10.1007/s10603-017-9354-z.

11 Cf DK Citron and FA Pasquale, ‘ The Scored Society: Due Process for Automated Predictions’

(2014) 89 Washington Law Review 1; T Zarsky, ‘Understanding Discrimination in the Scored Society’
(2014) 89 Washington Law Review 1375.
12 Cf R Swedloff, ‘Risk Classification’s Big Data (R)evolution’ (2014) 21 Connecticut Insurance Law

Journal 339; MN Helveston, ‘Consumer Protection in the Age of Big Data’, (2016) 93(4) Washington
University Law Review 859.
13 Cf N Fourberg et al., ‘Online advertising: the impact of targeted advertising on advertisers,

market access and consumer choice’, Study requested by the IMCO committee of the European
Parliament, PE 662.913 – June 2021, www.europarl.europa.eu/RegData/etudes/STUD/2021/662913/
IPOL_STU(2021)662913_EN.pdf.
14 O Bar-Gill, ‘Algorithmic Price Discrimination When Demand Is a Function of Both Preferences

and (Mis)perceptions’ (2019) 86(2) University of Chicago Law Review 217. Some scholars suggest that
AI-driven big data analytics can even allow for personalised legal rules that match individual needs and
22 Martin Ebers

Of particular concern are especially two aspects: First, AI techniques can be

used by companies for first-degree price discrimination.15 Sellers are increasingly
utilising big data and sophisticated algorithms to assess the customer’s willingness-
to-pay for their goods or services and to charge each customer a personalised
price that is as close as possible to the maximum price that each customer is
willing to pay. Personalised pricing can be both beneficial and detrimental. For
instance, personalised pricing allows firms to set a lower price and profitably sell
to customers that would not be willing to pay the uniform price that firms would
otherwise set. However, personalised pricing can also be inequitable because for
some customers, it will lead to higher prices than a uniform price. Moreover, price
discrimination can help to monopolise a market and to make market entry unat-
tractive for competitors.16
The second aspect concerns the use of AI systems to exploit behavioural biases
of customers: AI-driven big-data profiling techniques enable companies to manip-
ulate the choices of customers in a predetermined direction and even exploit their
biases,17 for example, by offering products or services exactly when customers
(due to the time of the day, a previous event or personal situation) can only make
suboptimal decisions, or by creating certain digital choice architectures and dark
patterns. The Cambridge Analytica case is a shining example of how the AI-driven
big data profiling can nudge undecided voters to gain political power.
Apart from the pre-contractual phase, AI contracting tools and chatbots are
also used to govern the contracting process itself, especially for negotiating and
drafting contracts.18 Whereas the first generation of Negotiation Support Systems
(NSSs) were mostly template-based and did not explicitly use AI techniques; the
current systems based on ML can advise the parties about their respective ‘Best
Alternative to a Negotiated Agreement’ (BATNAs) and hence facilitate the nego-
tiating process.19 In the field of Alternative Dispute Resolution,20 some providers

preferences and promote more efficient outcomes. Cf C Busch and A De Franceschi (eds), Algorithmic
Regulation and Personalized Law (C.H. Beck/Hart Publishing/Nomos, 2021).
15 First-degree price discrimination occurs when individual customers receive different prices

based on their individual preferences. In contrast, second-degree price discrimination refers to differ-
ent prices charged to different buyers depending on the quantity or quality of the goods or services
purchased, whereas third-degree price discrimination happens when different groups of consumers
receive different prices, for example in the case of coupons. Cf AC Pigou, The economics of welfare,
4th edn (Macmillan & Co., 1932); Borgesius and Poort (n 10) 351f.
16 Borgesius and Poort (n 10) 354.
17 Behavioural economics has been able to show that humans have only limited rationality which can

be exploited by choice architectures, for example, by presenting desired options as a preselected default
(status quo bias), by adding unattractive options (decoy effect), by positioning an option earlier/later
(primacy/recency effect), by positioning an option in the middle (middle option bias), by indicating
a sum of money (anchoring effect) or by personalising the choice environment for individual users
according to group profiles. See RH Thaler and CR Sunstein, Nudge: Improving Decisions About Health,
Wealth, And Happiness (Penguin Books, 2009).
18 For an overview of contract drafting solutions, cf ch 6 in this book.
19 J Zeleznikow, ‘Using Artificial Intelligence to provide Intelligent Dispute Resolution Support’

(2021) 30 Group Decision and Negotiation 789–812.

20 For a discussion of the use of AI systems in alternative dispute resolution schemes cf M Ebers,

‘Automating Due Process – The Promise and Challenges of AI-based techniques in Consumer Online
 Artificial Intelligence, Contracting and Contract Law: An Introduction 23

are also offering blind bidding processes where an automated algorithm evaluates
bids from parties, assessing whether they are within a prescribed range to settle the
case – a technique which can also be used for negotiating contracts.21
Additionally, argumentation support tools as well as decision support systems
might be helpful in negotiations. While an argumentation support tool helps the
parties (often by means of a dialog system) to improve the structure of the infor-
mation exchanged between them, decision support systems include rule-based
or case-based reasoning and ML, including neural networks, suggesting the best
strategy for optimal outcomes.
AI contracting tools can also be used for algorithmic (automated) decision-
making and formation of contracts.22 Nowadays, such systems can be found not
only in financial markets (eg for algorithmic trading), but also in other markets
(eg for sales, where an algorithmic system – and sometimes even a self-learning AI
system – is contracting on behalf a company).23
During the performance phase, AI systems facilitate and automatise the execu-
tion of transactions, assisting and simplifying real-time payments and managing
supply chain risks. They also play a crucial role in contract management and
due diligence.24 Companies can review and manage contracts faster and more
accurately by identifying terms and clauses that are suboptimal, and by flagging
individual contracts based on firm-specified criteria.
Finally, at the post-contractual phase, AI systems can help to litigate legal
disputes by handling customer complaints and resolving online disputes,25 or
predicting the outcome of court proceedings.26

Dispute Resolution’, in X Kramer et al. (eds), Frontiers in Civil Justice: Privatisation, Monetisation and
Digitisation (Edward Elgar Publishing, 2022), (forthcoming).
21 AR Lodder and EM Thiessen, ‘ The Role of Artificial Intelligence in Online Dispute Resolution’

Proceedings of the UNECE Forum on ODR 2003, www.mediate.com/Integrating/docs/lodder_

thiessen.pdf.
22 From the technical perspective, cf (in a chronological order) especially the following books:

S Ossowski (ed), Agreement technologies (Springer, 2013); M Rovatsos et al. (eds), Multi-agent systems
and agreement technologies – 13th European Conference, EUMAS 2015, and Third International
Conference, AT 2015, Athens, Greece, December 17–18, 2015, Revised Selected Papers (Springer,
2016); NC Pacheco et al. (eds), Multi-agent systems and agreement technologies – 14th European
Conference, EUMAS 2016, and 4th International Conference, AT 2016, Valencia, Spain, December
15–16, 2016, Revised Selected Papers (Springer, 2017); M Lujak (ed), Agreement technologies –
6th International Conference, AT 2018, Bergen, Norway, December 6–7, 2018, Revised Selected Papers
(Springer, 2019).
23 For an explanation on how AI is involved in the process of contract formation today and how it

may be involved in the future, cf ch 4 in this book.

24 Cf ch 5 in this book; see also R Schuhmann, ‘Quo Vadis Contract Management? Conceptual

Challenges Arising from Contract Automation’ (2000) 16(4) European Review of Contract Law
489–510.
25 The most prominent example is eBay’s ODR Resolution Center, which reportedly handles (auto-

matically) over 60 million disputes annually; AJ Schmitz and C Rule, The New Handshake: Online
Dispute Resolution and the Future of Consumer Protection (ABA Publishing, 2017) 53; C Rule and
C Nagarajan, ‘Leveraging the Wisdom of Crowds: The eBay Community Court and the Future of
Online Dispute Resolution’, ACResolution Magazine (Winter 2010).
26 KD Ashley, ‘A Brief History of the Changing Roles of Case Prediction in AI and Law’ (2019) 36(1)

Law in Context 93–112; M van der Haegen, ‘Quantitative Legal Prediction: The Future of Dispute
24 Martin Ebers

III. Freedom of Contract and Party Autonomy

Historically, contract law is based on the principle of freedom of contract and
party autonomy. As a rule, a natural and legal person should be free to decide
whether or not to contract, with whom to contract, and to agree freely on the
terms of their contract. The underlying idea is the assumption that freedom of
contract – under the condition that the parties to a contract are fully informed and
at an equal bargaining position – leads to justice: ‘Qui dit contractuel, dit juste’.27
However, even classic contract law recognises exceptions to these principles,
for example, if the contract was concluded as a result of mistake, fraud, duress
or the exploitation of a party’s circumstances to obtain an excessive advantage.28
In modern times, the growth of consumer protection as well as rent and employ-
ment legislation has restricted the parties’ freedom even further to restore
‘genuine’ contractual freedom.29 To this end, specific areas of law recognise certain
restrictions on the freedom of contract in order to remedy the inequality in the
bargaining power of parties, information asymmetries or unacceptable forms of
discrimination. Such interventions are most common in consumer law, however,
some legal systems even intervene in contracts between businesses, particularly
when one party is a small business lacking bargaining power.30
The rise of AI in contracting processes poses the question of whether contract
law needs to provide additional or new corrective mechanisms to address the new
power imbalances caused by AI systems. Obviously, the use of AI systems can
exacerbate existing power asymmetries, as parties to a contract can leverage them
to draft lopsided contracts and gain a better bargaining power.
From the standpoint of consumers and other customers, one of the most
troubling developments is the growing information asymmetry between provid-
ers and customers. In many cases, customers remain oblivious to personalised
advertisements, information, prices, or contract terms based on big data profil-
ing. If, for example, a business refuses to conclude a contract or makes an offer
with unfavourable conditions because of a certain customer score, the customers

Resolution?’, in J De Bruyne and C Vanleenhove (eds), Artificial Intelligence and the Law, (Intersentia,
2021), doi.org/10.1017/9781839701047.005, 73–99; DM Katz, ‘Quantitative Legal Prediction –
or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal
Services Industry’ (2013) 62 Emory Law Journal 909–966; M Scherer, ‘Artificial Intelligence and Legal
Decision-Making: The Wide Open?’ (2019) 36(5) Journal of International Arbitration 539–574, 547ff.
27 A Fouillée, La science sociale contemporaine (Hachette et cie, 1885) 410.
28 J Cartwright and M Schmidt-Kessel, ‘Defects in Consent: Mistake, Fraud, Threats, Unfair

Exploitation’ in G Dannemann and S Vogenauer (eds), The Common European Sales Law in Context
Interactions with English and German Law (OUP, 2013), 373–422.
29 PS Atiyah, The Rise and Fall of Freedom of Contract (Clarendon Press, 1979).
30 This applies in particular to the legislation on unfair contract terms, which in many countries

also applies to contracts between businesses; for a comparative overview in the EU cf M Ebers, ‘Unfair
Contract Terms Directive (93/13)’, in H Schulte-Nölke et al. (eds), EC Consumer Law Compendium,
(Sellier European Law Publishers, 2008) 197–261, 221ff.
 Artificial Intelligence, Contracting and Contract Law: An Introduction 25

are usually left in the dark from understanding how this score was achieved in
the first place. This asymmetry arises not only because the algorithms used are
well-guarded trade secrets, but also because the specific characteristics of many AI
technologies31 – such as opacity (the ‘black box effect’), complexity, unpredictabil-
ity and semi-autonomous behaviour – can make effective enforcement of rights
more difficult, as the decision cannot be traced and therefore cannot be checked
for legal compliance.
On the other hand, consumers and other customers can also use algorithms
to make and execute decisions by directly communicating with other systems
through the internet. Such algorithmic systems (eg shopping bots) can significantly
reduce search transaction costs, avoid consumer biases, overcome manipulative
marketing techniques, enable more rational and sophisticated choices, and create
or strengthen buyer power.32 Moreover, Legal Tech companies using intelligent
algorithmic systems can also help consumers to enforce their rights. For example,
companies such as Do-Not-Pay,33 Flightright34 or RightNow35 help consumers at a
large-scale to enforce small claims which otherwise would not have been brought
to court due to relatively high legal fees and the well-known problem of ‘rational
apathy’. Additionally, consumer organisations and public watchdogs can use Legal
Tech services to monitor and enforce existing consumer law,36 for instance for
detecting unfair contract terms in online contracts.37 As a result, Legal Tech offers
the possibility to strengthen the rule of law, to reduce existing cost barriers, to
open-up latent markets and create new areas of competition.38
Consequently, (contract) law may not always proffer instruments for balancing
power and information asymmetries in favour of customers. The more self-help
is used by customers, the less corrective intervention in the market mechanism
is required. However, such a self-help is rather unlikely in case of consumers who
purchase goods or services for private purposes. As an observation, only technol-
ogy literate people and companies can be expected to use software tools to enhance
their bargaining power and improve their decision-making process.39

31 European Commission, White Paper ‘On Artificial Intelligence – A European approach to excel-

lence and trust’, COM(2020) 65 final, 14.

32 MS Gal and N Elkin-Koren, ‘Algorithmic Consumers’ (2017) 30(2) Harvard Journal of Law &

Technology 309–352.
33 www.donotpay.com.
34 www.flightright.de.
35 www.rightnow.de.
36 G Contissa et al., ‘ Towards Consumer-Empowering Artificial Intelligence’, Proceedings of

27th IJCAI Conference 2018, 5150-5157, doi.org/10.24963/ijcai.2018/714.

37 http://claudette.eui.eu/about/index.html.
38 Cf M Ebers, ‘Legal Tech and EU Consumer Law’, in LA DiMatteo et al. (eds), The Cambridge

Handbook of Lawyering in the Digital Age (Cambridge University Press, 2021), 195–219, ssrn.com/
abstract=3694346.
39 Likewise, D Schäfers, ‘Rechtsgeschäftliche Entscheidungsfreiheit im Zeitalter von Digitalisierung,

Big Data und Künstlicher Intelligenz’, (2021) 221 Archiv für die civilistische Praxis (AcP) 32–67, 51.
26 Martin Ebers

IV. Pre-contractual Duties

A. Contract Law, Fair Trading Law and Data Protection Law
All over the world, legal systems have established pre-contractual duties that
(potential) parties to a contract have to observe before entering into a contract.40
In the European Union, many consumer law directives establish pre-contractual
duties for businesses – by prohibiting unfair commercial practices, such as mislead-
ing advertisements or by establishing information duties – in order to allow the
consumer to make an informed decision before concluding a contract.41 In addi-
tion, the General Data Protection Regulation (GDPR)42 protects data subjects
against having their personal data misused by companies.
Against this backdrop, the question arises as to what extent these legal regimes
can help to mitigate the aforementioned two problems – price discrimination and
the exploitation of customer bias.
When assessing this question, it is important to distinguish between contract
law on the one hand and other bodies of law – such as fair-trading law (UCPD)43
and data protection law (GDPR) – on the other. The UCPD primarily aims to
protect market participants against unfair commercial practices; however, the
Directive is not designed to provide contractual remedies to individual consum-
ers in the event that an unfair commercial practice leads to the conclusion of
contracts.44 In the same vein, the GDPR protects fundamental rights and freedoms

40 For a comparative overview, cf R Sefton-Green (ed), Mistake, Fraud and Duties to Inform in

European Contract Law (Cambridge University Press, 2005); H Fleischer, ‘Informationsasymmetrie

im Vertragsrecht’, (CH Beck, 2001). See also R Schulze et al. (eds), Informationspflichten und
Vertragsschluss im Acquis communautaire – Information Requirements and Formation of Contract in the
Acquis Communautaire (Mohr Siebeck, 2003).
41 For a detailed analysis of the pre-contractual duties in EU Private Law, cf M Ebers, Rechte,

Rechtsbehelfe und Sanktionen im Unionsprivatrecht (Mohr Siebeck, 2016), 798ff; C Busch,

Informationspflichten im Wettbewerbs- und Vertragsrecht (Mohr Siebeck, 2008); T Wilhelmsson and
C Twigg-Flesner, ‘Pre-contractual information duties in the acquis communautaire’ (2006) 2(4)
European Review of Contract Law 441–470.
42 Regulation 2016/679 of 27 April 2016 on the protection of natural persons with regard to the

processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC
(General Data Protection Regulation), [2016] OJ L119/1.
43 Directive 2005/29/EC of the European Parliament and of the Council of 11 May 2005 concern-

ing unfair business-to-consumer commercial practices in the internal market and amending Council
Directive 84/450/EEC, Directives 97/7/EC, 98/27/EC and 2002/65/EC of the European Parliament and
of the Council and Regulation (EC) No 2006/2004 of the European Parliament and of the Council
[2005] OJ L149/22 (Unfair Commercial Practices Directive) (UCPD).
44 According to Art 3(1) UCPD 2005/29, the Directive is ‘without prejudice to contract law and,

in particular, to the rules on the validity, formation or effect of a contract’. Additionally, recital (9)
UCPD clarifies that the Directive ‘is without prejudice to individual actions brought by those who
have been harmed by an unfair commercial practice’. Although Art 3(5) Modernization Directive
2019/2161 introduced a new Art 11a in UCPD to make available a right to damage and, where relevant,
the right to price reduction or unilateral termination of the contract, the conditions under which the
consumer can make use of these remedies remain largely unclear; cf MBM Loos, ‘The Modernization
 Artificial Intelligence, Contracting and Contract Law: An Introduction 27

of natural persons and in particular their right to the protection of personal data
(Article 1(2) GDPR), but does not deal with the conditions for contracts to be
legally binding. While both UCPD and GDPR45 as well as other tools (eg opt-out
rights46) can help mitigate the problems of price discrimination and exploitative
contracts, neither legal regime addresses the contractual remedies that apply in
these cases.

B. Pre-contractual Duties and Price Discrimination

From a contract law perspective, price discrimination primarily raises the ques-
tion of whether companies must disclose the application of personalised prices.
Such an obligation indeed seems appropriate.47
EU consumer law already recognises such an obligation, albeit with certain
exceptions. Article 6(1)(ea) Consumer Rights Directive (CRD) 2011/83/EU48
as amended by Modernization Directive 2019/2161/EU,49 requires the trader
to inform the consumer ‘that the price has been personalised on the basis of
an automated decision making process’. At the same time, however, recital (45)
Modernization Directive 2019/2161 states that
‘[t]his information requirement should not apply to techniques such as ‘dynamic’ or
‘real-time’ pricing that involve changing the price in a highly flexible and quick manner
in response to market demands when those techniques do not involve personalisation
based on automated decision-making.

of European Consumer Law (Continued): More Meat on the Bone After All’, (2020) 28(2) European
Review of Private Law 407–423, 408f.
45 For an analysis of the UCPD 2005/29 and the GDPR see M Ebers, ‘Beeinflussung und Manipulation

von Kunden durch “Behavioral Microtargeting”’ (2018) MultiMedia und Recht 423; F Galli, ‘Online
Behavioural Advertising and Unfair Manipulation Between the GDPR and the UCPD’ in M Ebers and
M Cantero (eds), Algorithmic Governance and Governance of Algorithms (Springer, 2020) 109–135;
Helberger (n 9) 135 ff.; E Mik, ‘The Erosion of Autonomy in Online Consumer Transactions’ (2016)
8(1) Law, Innovation and Technology 1, ink.library.smu.edu.sg/sol_research/1736.
46 In favour for a right of the consumer to opt out of personalised pricing by clicking on a stop button:

G Sartor, ‘New aspects and challenges in consumer protection’, Study requested by the IMCO committee,
PE 648.790 – April 2020, 35, www.europarl.europa.eu/RegData/etudes/STUD/2020/648790/IPOL_
STU(2020)648790_EN.pdf; G Wagner and H Eidenmüller, ‘Down by Algorithms? Siphoning Rents,
Exploiting Biases, and Shaping Preferences: Regulating the Dark Side of Personalized Transactions’,
(2019) 86(2) University of Chicago Law Review 581–609.
47 Wagner and Eidenmüller (n 46).
48 European Parliament and Council Directive 2011/83/EU of 25 October 2011 on consumer rights,

amending Council Directive 93/13/EEC and Directive 1999/44/EC of the European Parliament and
of the Council and repealing Council Directive 85/577/EEC and Directive 97/7/EC of the European
Parliament and of the Council [2011] OJ L304/64.
49 European Parliament and Council Directive 2019/2161 of 27 November 2019 amending Council

Directive 93/13/EEC and Directives 98/6/EC, 2005/29/EC and 2011/83/EU of the European Parliament
and of the Council as regards the better enforcement and modernization of Union consumer protec-
tion rules [2019] OJ L328/7.
28 Martin Ebers

Moreover, Article 6(1)(ea) CRD does not require the trader to reveal the algorithm
nor how the price has been adjusted for a particular consumer.50 Consequently, it
is unlikely that the consumer will notice any price discrimination.
If, on the other hand, such information obligations were recognised under
(consumer) contract law, price discrimination could be sanctioned by damages:
Consumers would then have to be placed in the position they ought to have been
in, if they had been made aware of the dynamic pricing: If the consumers can show
that, upon being properly informed about the dynamic prices, they would have
refrained from the contract and instead would have concluded another contract
(with the same company or another provider) at a lower price, the trader would
have to reimburse the consumer for the difference between the dynamic and the
lower price.

C. Pre-contractual Duties and Exploitative Contracts

In contrast to the pre-contractual duties applied to price discrimination under
contract and/or consumer law, ‘exploitative contracts’, require other legal instru-
ments to effectively remedy the problem. If a company exploits biases of the other
contracting party or even actively induces them into unfavourable terms, then the
disclosure obligations simply appear futile. Information requirements are largely
ineffective if the recipient is unable to make a rational decision due to behavioural
biases. The legal system must therefore find alternative solutions. In the event that
one of the parties to a contract is unable to oversee the consequences of its actions,
contract law typically provides for various defences based on doctrines of duress,
mistake, undue influence or misrepresentation that focus on the perspective of the
aggrieved party. However, these legal institutions do not provide a remedy if wide-
spread or highly individual biases are exploited by companies:51 The defence of
duress is not applicable, since algorithmic manipulation does not involve a wrong-
ful or illegitimate threat. ‘Mistake’ is also not an available defence for the customers,
since the error must relate to specific contractual clauses. The equitable doctrine
of ‘undue influence’ requires a relationship of trust or confidence between parties,
which is mostly missing in AI-based contracts. ‘Misrepresentation’ requires false
statements, which are also usually not present in the case of AI-based exploitative
contracts.
Owing to the said deficiencies in law to remedy the problem of exploitative
contracts, legal scholars thus argue in favour of recognising a ‘right to cancel the
contract’52 if: (i) the customer has significant rationality deficits; and (ii) the trader

50 A Reyna, ‘ The Price Is (Not) Right: The Perils of Personalisation in the Digital Economy’, Informa-

Connect, 4 January 2019, informaconnect.com/the-price-is-not-right-the-perils-of-personalisation-

in-the-digital-economy/.
51 Cf Mik (n 45).
52 Ebers (n 45).
Another random document with
no related content on Scribd:
railway grow mile by mile, and their interest in it was almost that of a
proprietor, but this was the first time they had ridden upon it for any
distance.[9] Most of the units halted for a day or two at Kantara, a
station with which they were familiar enough. Here the only subject
for comment seems to have been the remarkable number of gulls
that swarmed overhead at meal times. The mention of these birds
will remind many officers and men that the 42nd Division made very
useful contributions both to the knowledge of the fauna of the Sinai
Peninsula and to the supply of animals to the Cairo Zoo. Many
desert mice and rats, lizards and tortoises reached the Zoo alive,
and one rat was so exalted by the prospect of introduction to Cairo
society that it gave birth to a healthy litter while in the parcel post.
Insects of great interest and rarity, and of peculiarly local distribution,
were sent to the Ministry of Agriculture at Cairo twice a week for six
months; and species entirely new to science were discovered. A
battalion of the Lancashire Fusiliers, from the R.S.M. and the Cook-
Sergeant down to the sanitary men, took to collecting and nature
study with great ardour and much success.
Divisional Headquarters and the Signal From East to
Company arrived at Moascar on February 4. On West
the 6th, 7th, and 8th the various units (less the 2nd
Field Company, R.E., which proceeded direct by rail to Alexandria)
set out from Kantara on the two-days’ march to Moascar along the
new road by the side of the Canal. The change from the soft sand of
the desert to the hard road was a sore trial to the feet, and a big
proportion of the men limped rather than marched into Moascar. All
ranks now knew what most had suspected for some time, that the
Division was bound for France, and there was general enthusiasm.
The prospect of a change from the sand, the glaring sun, the
discomfort of intense heat, the monotony and isolation of the desert,
was hailed with joy by the majority. A number of officers and men
had not been home since September 1914, and knew that there was
little chance of home-leave while the Division remained with the
Egyptian Expeditionary Force. Yet there were some among those
who had been out longest upon whom the spell of the East had
fallen, and who were disappointed that, having accomplished so
much of the preparatory work, they, like Moses, could only see the
Promised Land from afar, and were not allowed to go forward into
Palestine.
While at Moascar the Division was inspected by Lieut.-General Sir
Charles Dobell, commanding the Eastern Force, and it also marched
past General Sir Archibald Murray, Commander-in-Chief of the
Egyptian Expeditionary Force. An event of even greater moment for
men who had been nearly twelve months in the desert was a week’s
visit from Miss Lena Ashwell’s Concert Party. The troops were
grateful and appreciative, and they showed it unmistakably.
FLASHES OF UNITS IN THE 42ND DIVISION.
 In view of the impending change every man now required serge
clothing, winter underclothing and service cap. The field-gun
batteries were established on a six-gun basis, and two artillery
brigades, the 210th and 211th, were formed out of the three existing
brigades of four-gun batteries. The 1st, 2nd, and 3rd Field
Companies, R.E., were re-numbered as the 427th, 428th, and 429th
Field Companies respectively. Many details left behind during the
advance across the desert rejoined the Division, as did also the R.A.
Base at Ballah, and the instructors and staff of the Divisional School
at Suez. This school had done most useful work, a large number of
officers and other ranks having been put through a series of short
courses, and much progress had been made in bombing and in the
use of the Light Trench Mortar, or Stokes Gun.
To the great disappointment of all ranks it was decided that the
A.S.C. should remain in the East, as a new 42nd Divisional Train had
been formed in England to join the Division on its arrival in France.
There was sincere regret on both sides at the severing of
comradeship. The Divisional Train left Kantara early in March to join
the 53rd Division, to which it was attached during the operations
against Gaza. On the formation of the 74th (Yeomanry) Division it
became the 74th Divisional Train, took part in all operations with that
Division in Palestine, went with it to France, and remained with it
until disembodied. The Divisional Squadron, now with the 53rd
Division, was engaged in the first and second battles of Gaza. Later,
it was attached in turn to the 60th and 52nd Divisions in Palestine
and Syria; it took part in the third Battle of Gaza, in numerous
skirmishes, outpost affrays, and pursuits, and shared in the honour
of the great campaign that brought Turkey to her knees.
Before the end of February all preparations had been completed,
and units had entrained for Alexandria. On March 2, 1917, the last
transport left the harbour, and, after two and a half years of service in
the Near East, the 42nd Division was at last on its way to the
Western Front.
 CHAPTER V
FRANCE
(March-August 1917)

The voyage westward across the Mediterranean was made under

conditions widely different from those of the outward journey of
September 1914, when “glory of youth glowed in the soul,” and the
glamour of the East and the call of the unknown had made their
appeal to adventurous spirits. Familiarity with war had destroyed
illusion and had robbed it of most of its romance. The Lancashire
Territorials had a very good idea of what to expect in France or
Flanders, and were prepared to face minor discomforts and worries
with the inevitable grousing which proclaims that all is well, and real
privations, perils, and horrors with steadfastness often masked by
levity. Though the Mediterranean was at that period infested by
enemy submarines, the vigilance of the British and French navies
proved a sure shield. One torpedo only was fired at the troopships,
and this passed between the log-line and the stern of the Megantic.
A call was made at Malta, and on March 1 the first transport
anchored in the magnificent harbour of Marseilles, and D.H.Q. at
once entrained for the North of France.
The railway journey of sixty hours to Pont Remy, near Abbéville,
will not be forgotten. Men who had at much cost become
acclimatized to the intense heat and dryness of the Sinai Desert,
were suddenly plunged into the opposite extreme of an arctic
climate. The winter of 1917 was one of the most prolonged and
severe on record, and throughout the tedious journey in French
troop-trains the men shivered and trembled with the bitter cold. But if
France greeted them freezingly there was no mistaking the warmth
of the welcome of her sons and daughters. Wherever the trains
stopped the inhabitants gathered round to cheer them on their way.
The news of the fall of Bagdad had preceded them, and the French
women and girls, old men and children, knew that these were
victorious British reinforcements from the East, and Bagdad and
Sinai were equally remote.
The troops detrained at Pont Remy in a storm of snow and sleet,
and marched through deep, freezing slush to the villages in which
billets had been prepared. After six months’ experience of open
bivouacs wherever the day’s trek ended, the barn billets were
something of a novelty. Reorganization and re-equipment were, of
course, the most urgent matters to be dealt with, and the refit was
carried out expeditiously. The short Lee-Enfield rifle displaced the
longer rifle with which the Division had been armed; and the issue of
two strange items, the “tin hat” and the box respirator, provoked
some hilarity. Baths, each capable of washing sixty men per hour,
were erected by the R.E., and henceforward the Division left its mark
in the shape of new or remodelled baths in every area in which it
was located. The Divisional Cinematograph and Canteen were also
inaugurated here. The last troops from Egypt, the 5th East
Lancashires and the 9th Manchesters, arrived on March 15. A new
Divisional Train joined from England. This train had already had
considerable experience of France, as it had been formed to join the
Lahore Division in September 1914. Motor ambulances were
supplied to the three Field Ambulances, and a complete train of
motor-lorries was attached to the Division. The 42nd Divisional
Ammunition Column was formed from a nucleus of the former
Brigade Ammunition Columns with the addition of a large draft from
the R.A. Base in France. A Heavy (9·45-inch) Trench Mortar Battery
and three Medium (6-inch) T.M. Batteries were also formed here,
and these became a part of the Divisional Artillery. Three Light T.M.
Batteries were attached to the Infantry Brigades.
On the arrival of the Division in France Major- General
General Sir William Douglas left for England in Douglas’s
order to give evidence before the Royal Farewell
Commission appointed to inquire into the Dardanelles Campaign.
Temporary command of the Division was taken over by Brig.-General
H. C. Frith, C.B. (125th Brigade), until the arrival of Major-General B.
R. Mitford, C.B., D.S.O., who assumed command about the middle
of March. Much regret was felt by officers and men that the general,
who had been responsible for the training and organization of the
Division in time of peace, and under whose leadership during two
and a half years of war it had served with distinction in two
campaigns and had “made good,” should be unable to lead them to
the gaining of fresh laurels on the most important of all fronts. They
had been fortunate in a commander who had ever taken a personal
interest in the welfare of all ranks under his command, and who had
identified himself with the Lancashire men and was jealous of their
good name. That General Douglas regarded his officers and men
with affection is clearly shown in his farewell message—

“In bidding the 42nd Division good-bye I wish to express my

heartfelt thanks to my Staff Officers, Commanders, and
Regimental Officers for their loyal and whole-hearted support
and superb work during the period of my command. My
admiration for the conduct, fighting qualities, grit, and
endurance of all ranks is profound. Never have I met a more
responsive, willing and lovable lot of men than these
Lancashire lads, and, to my last days, I shall remember with
affection and pride the three and three-quarter years that I
have had the honour to command them. I know how well you,
officers and men, will add to the great name you have already
earned for the Division, I wish you the best of good fortune
and a rich reward.”

Towards the end of March the Division moved to an area some ten
miles east of Amiens, D.H.Q. being established at Mericourt. The
42nd was now a veteran Division in war and in travel, but in the
trenches of France it was in the position of a new boy at a strange
school. It had learnt much in the old school, and the experience
would be useful. Each unit had a record and tradition of which it had
good reason to be proud, and the commanders knew that their
officers and men could be relied upon. Endurance and courage had
been severely tested, but the endurance required for slogging
through deep sand under a tropical sun was of a very different
nature from that which would now be demanded, and the intense
heat of the desert was a poor preparation for the bitter winds, the
snow, sleet and freezing mud of the trenches of France. Much had to
be learnt in the new school, and much unlearnt.
In Gallipoli the opposing trenches had often been only a few yards
apart, and rifle-fire had continued all day and increased in violence at
night. In that sector of the Western Front taken over by the Division
the recent withdrawal of the enemy had created a No Man’s Land,
which might be anything from 10 yards to 1000 in width, and
unaimed rifle-fire was uncommon. Here, too, patrolling was a matter
of nightly routine, whereas in Gallipoli more than an occasional patrol
had been impossible. Two of the most novel features were the gas
and the amount of H.E. shelling. It was the Division’s first experience
of gas, and on rare occasions only had it witnessed annihilating
shell-fire. Never before had any of the original members been in
billets, and they found them and their inhabitants a source of interest
and comfort. Some felt hurt that the bits of Arabic picked up in the
East were of no use here, and they resolutely refused to learn any
French. “I’ve learnt Gyppo, and I’m not going to bother with any more
foreign languages.” Imagine their delight when on leave in Amiens
they found that the paper-boys (who had come into contact with the
Australians) knew the meaning of “Imshi!” This word, being the
imperative of the Arabic verb “to walk,” did duty for “’op it!” Possibly
the most striking differences of all were that the Division got
reinforcements after suffering casualties, and was able to get back
into “rest” of a real kind after a trying time in the line.
The strength of the Division on April 1 was 727 officers and 16,689
other ranks.
Advance parties had been sent ahead of the New Experiences
Division, and now other parties of officers, N.C.O.s
and men were attached for short periods to battalions and units of
the 1st Division in the front line trenches that they might see and
understand the conditions of warfare on the Western Front, before
the Division should be called upon to take its place in the line that
stretched from the Belgian coast to Switzerland. The enemy’s
retirement from the Somme and the Ancre to the Hindenburg Line
had upset the plans of the Allies for a spring offensive. The recently-
vacated German trenches were visited, and the scenes of appalling
devastation, the shattered remains of what had once been
flourishing villages and farmsteads, gave the troops their first
impressions of France’s martyrdom, and filled them with indignation
and loathing. They had heard and read of the ruin and desolation in
Belgium and Northern France, but the half had not been told. The
wanton destruction of fruit-trees and the desecration of cemeteries
were acts dictated not by military necessity but by beastliness of
mind.
Throughout this preparatory period the troops were kept busily
employed upon the badly damaged roads, and—as occasional
opportunity offered—in the attempt to make the entente still more
cordiale. Feuillieres, Biaches, Herbecourt, Flaucourt, Dompierre, and
Peronne were visited by various units, and the sappers constructed
bridges to take heavy guns and lorry traffic over the Somme at Brie
and elsewhere. Not only had the enemy blown craters at most of the
cross-roads, but, east of Peronne, he had felled the trees that line
the main French roads, and these had to be removed. This work of
clearing up after the German retreat was of great importance, and
the Division gained an insight into conditions on the Western Front
as the troops approached the line. Where possible the ruins of farms
and houses, swarming with rats, were used as billets, but the road-
makers usually slept in cellars, dugouts, and holes. The wretched
weather continued and there was heavy snow in April. The horses,
so long accustomed to an eastern climate, suffered greatly and
began to deteriorate, some succumbing to pneumonia. The boots
which had been issued just before leaving Egypt were quite unsuited
to a bad winter in Northern France, and they fell to pieces quickly.
Each day a number of men had to remain in billets until new boots
could be obtained from Ordnance Stores. A number of officers and
men, however, refused to be worried by such insignificant details as
boots, for were they not going home for the first time since
September 1914? During the month batches of these veterans
departed for fourteen days amid the rousing cheers of their
comrades.
 At Peronne, where D.H.Q. was opened on April 14, every building
was badly damaged except the Town Hall, which was at once placed
out of bounds because of this immunity, as any place that appeared
to invite occupation was regarded with suspicion, owing to the typical
Boche habit of leaving delayed-action mines and other “booby-
traps.” Peronne Town Hall did not, however, go sky-high, as was
daily expected. In the village of Peiziere some officers of the 126th
Brigade took up their quarters in a house that had been left in good
condition. Fortunately one of them took the precaution to explore and
found a quantity of high explosive hidden under the beams. They
cleared out. Next day a shell dropped on the building and it
vanished. An R.A.M.C. orderly in the vicinity was lifted several feet in
the air by the force of the explosion. “Eh, that wur a near do!” he
said, as he picked himself up carefully and resumed his journey.
The Division now formed part of the 3rd Corps of the Fourth Army.
On the 8th of April the 125th Brigade took over a portion of the line
from the 48th Division at Epéhy, in front of Le Catelet, and a few
days later the 126th Brigade also went into the line, in order that as
many battalions as possible might have a short experience of front-
line conditions before the Division as a whole assumed responsibility
for a sector. The front here had become practically stationary, and as
neither side had a continuous trench system the connecting of posts
proceeded nightly, and patrolling and digging were the chief
diversions. The 7th Lancashire Fusiliers was the first battalion to go
into the line, which they advanced, after a sharp skirmish, to a copse
about half a mile ahead. They were relieved on April 12-13 by the
6th L.F., and during the relief Malassise Farm, in which were a
number of men of both battalions, was heavily shelled. The building
was destroyed, and the fall of the roof buried about fifty of these men
in the cellar. Though the shelling continued with great violence,
admirable courage was shown in extricating the buried men, and for
this the Military Medal was awarded to a private of each battalion.
The Division’s first trench raid on the Western Front was made by
the 4th East Lancashires at Epéhy. On April 28 the 126th Brigade
advanced their line successfully, but the 4th and 5th East
Lancashires suffered rather heavily.
 Throughout April the wintry weather continued, but the unfailing
spirit of the British soldier under depressing conditions is shown in
the following anecdote related by an officer of the 4th East
Lancashires: “The rain was pouring into my dugout, and the water
slowly rising, so to avoid a fit of the blues I went along the line to see
how the men were faring. A sentry was standing in mud half up to his
knees, his hands numbed and wet, and a stream of water ran from
his tin hat. By way of comparing notes I asked this pitiable spectacle
what he really felt like. ‘Like a flower in May, sir,’ was the cheerful
reply, and I was cured of the blues.”
On May 3 the Division took over from the 48th Division a sector in
the neighbourhood of Ronnsoy, south-east of Epéhy. As Brig.-
General Ormsby was engaged in marking out the new front line of
his Brigade near Catelet Copse, the enemy suddenly opened a
bombardment, and he was struck in the head by a piece of shell and
killed. General Ormsby had been in command of the Brigade for
more than twelve months, and during that period he had become
very popular with his men and had gained their respect and
admiration. Lieut.-Colonel H. C. Darlington, 5th Manchesters, once
more assumed temporary command until the arrival of Brig.-General
the Hon. A. M. Henley, who remained in command of the 127th
Brigade until the end of the war.
Two brigades were in the front line and one in reserve, with a
system of four-day reliefs. The long winter was over at last; summer
had arrived without any introduction by spring, and the weather was
now gloriously hot. There was a good deal of local fighting,
especially around Guillemont Farm, an enemy post which more than
one division had found by no means difficult to capture, but
exceedingly difficult to hold. Several night attacks were made by
companies and platoons, in one of which, on the night of May 6-7,
the 9th Manchesters established forward posts in the face of heavy
machine-gun fire, and Private A. Holden was awarded the Bar to the
M.M. for volunteering to bring in the wounded, and afterwards going
out into the open to make sure that none had been missed. He found
a wounded officer and helped to carry him 400 yards on a heavily
shelled road, and went out again to assist another injured man to
safety. He succeeded in this, but was himself wounded. The enemy
artillery was generally active, and on one occasion some men of the
126th Brigade were quite grateful to the German gunners. A heavy
shell, which fell among some ruined cottages, threw up a number of
gold and silver coins, dated a hundred years ago and evidently a
long-buried hoard.
On May 23 D.H.Q. moved to Ytres, about eight Epéhy and Ytres
miles north-west of Epéhy, the Division relieving the
20th Division on a newly-captured sector running from the Canal du
Nord, south-west of Havrincourt, to a point south of Villers Plouich,
through Trescault and Beaucamp; and here the Division remained
until July 8. This was a fairly quiet sector, and during the first few
weeks there was no event of any importance to vary the daily round
of digging, wiring, and strengthening the trench system and the
patrolling of No Man’s Land. Havrincourt Wood in the spring of 1917
remained a very beautiful spot amid the chaos of war. Though the
“hate” of the Boche was less demonstrative than in many sectors his
trench-mortars and machine-guns were generally busy at night, and
considerable annoyance was caused on the right of the line by a
trench-mortar which—so it was conjectured—was brought up every
night on a light railway, and taken back after a few shots had been
fired. At sunrise the clamour of the guns ceased and the birds at
once “took over,” the cuckoo being particularly active. Nightingales
were common here and in the copses in the line, and as they
seemed to regard machine-guns as rival vocalists, they would sing in
competition. The bell-like whistle of the black and yellow golden
oriole was often heard, and in the centre of the wood the war at
times seemed far enough away. The A.S.C. turned their hands to
hay-making, and helped to cut and harvest some acres of excellent
clover, rye, and lucerne. The 3rd Field Ambulance were more envied
by their fellows, as they harvested—for their own consumption—the
crop of a very prolific strawberry bed in the garden of the ruined villa
which they inhabited at Ruyalcourt.
A quartermaster of the 127th Brigade had chosen the ruins of a
farm at a cross-roads near Havrincourt Wood for his dump. He was
warned by the Town Major that this spot had probably been mined by
the enemy, and particularly warned not to make use of the cellar,
which was a likely place for a “booby-trap.” However, nothing
happened, and of course his men not only went into the cellar but
took planks and bricks therefrom to improve their quarters in the
rooms above. One evening the Q.M. returned from the line to find his
staff in a state of nervous collapse. As soon as he had prevailed
upon them to sit up and take a little nourishment they related this
painful story: The former owner, armed with documents and
accompanied by gendarmes and British Military Police, had visited
the old home, descended into the cellar, and dug up jars containing
jewellery, coins, and banknotes, within a few inches of the spot from
which the storemen had taken the planks. The butcher had even
held a candle to assist the search, and his reflections on “what might
have been,” as the jars of buried treasure were brought to light,
completely unnerved him, especially when the owner handed him a
couple of francs with thanks for the trouble he had so kindly taken.
For some time after this these storemen displayed a rabbit-like
tendency to burrow in any old corner, but luck was not with them.
One night when the Brigadier of the 127th Brigade was in the front
line the enemy put down a fierce bombardment of gas shells and
H.E. The night was dark, but calm and clear, and large working
parties were out wiring and digging. These came back “hell for
leather,” and General Henley found his passage through the trench
cut off by the crowds. Colonel Dobbin, deeming the scene unseemly
for a Brigadier, suggested a dash over the top. Unfortunately fresh
wire had just been put down, and, close to the support line where the
long-range shells were dropping, both fell heavily into a double
apron-fence. They extricated themselves painfully, leaving portions
of clothing and some blood on the wire, and eventually arrived,
“improperly dressed,” at Battalion Headquarters, to be met by the
adjutant with the tactless remark: “There has been a bit of a
bombardment, sir, but it doesn’t concern our front.” The Brigadier,
who limped for several days, suggested that his companion should
write a sketch of the episode under the title, “Young officers taking
their pleasures lightly.” Though the Colonel did not take advantage of
the suggestion, another officer did.
 Brig.-General H. C. Frith, C.B., returned to England in June to
assume command of a Home Service Brigade, and Brig.-General H.
Fargus, C.M.G., D.S.O., took command of the 125th Brigade until
the end of the war. General Frith was the last of the General Officers
who had served with the Division from the outbreak of war. For three
years he had commanded the Lancashire Fusilier Brigade, which
had become much attached to him, for he was quick to recognize
and give credit for good work, and he possessed a remarkable
memory for faces, invariably knowing each officer by name after the
first meeting. The 6th Manchesters learnt with regret that their
popular M.O., Captain A. H. Norris, M.C., who was home on leave,
had been retained by the War Office for duty at home. A better-
known and better-liked Medical Officer never served with any
battalion, and the regret was not confined to officers and men of the
battalion, for the sick and wounded of many units were grateful for
the energy, solicitude and complete disregard of self—and of red
tape—which he had displayed in looking after their comfort and
welfare in Egypt, Gallipoli, Sinai, and France.
On the 1st of June the order was received to The Front
advance the divisional front by about 300 yards, Advanced, June
the operation to be completed by 6 a.m. on the 1917
10th. The order indicated that strong opposition might be expected,
and details were left to the Brigadiers. The 126th Brigade on the right
adopted the orthodox method of sapping forward each night, making
a T-head at each sap to connect and form a continuous line later.
The expectations of opposition were realized. Photographs taken by
enemy planes brought heavy trench-mortar and machine-gun fire on
the working-parties, and serious casualties were inflicted. A position
near Femy Wood was occupied at night by the enemy, who were
thence able to harass the working-parties. On the evening of June
3rd Corporal A. Eastwood, 9th Manchesters, took a patrol of three
men to this point and lay down to await events. At 9.30 p.m. a
German patrol emerged from the wood. The corporal ordered his
men to hold their fire until the enemy were within thirty paces, when
they opened fire with good effect, and remained until 2.30 a.m.
covering the work and silencing a machine-gun and snipers. The
hard and rocky nature of the ground in this part of the line was a
further obstacle, but in spite of all difficulties good progress was
made, and the troops were complimented upon their work by the
Chief Engineer of the Corps. On the left, Brig.-General Henley,
profiting by the experience of the 126th Brigade, decided to complete
his part of the operation at one bound. On the night of the 8th-9th he
advanced his line the full distance, and all four battalions of the
127th Brigade began to dig in furiously. The covering party was in
position at 10.30 p.m., and digging began at 11 p.m. under the
supervision of the 427th Field Company, R.E. Before dawn twelve
outposts on a front of 1500 yards were linked up by a continuous
trench, and, leaving a skeleton garrison in the new trench, the
companies returned to their positions practically unharmed. The
finishing touches were added next night, and the new line was
completed by the stated hour. This good work was rewarded by a
Special Order of the Day from the Corps Commander.
The night patrolling in No Man’s Land furnished admirable
opportunities for testing and training officers and men. These patrols
appealed to many adventurous spirits, while others looked forward to
their first experience with natural apprehension. Many patrols were
therefore sent out with the primary object of giving the men
confidence and experience, and this policy was completely
successful. There was also a considerable amount of sniping,
especially in the vicinity of Havrincourt Wood, where German snipers
for a time had the advantage and made the most of their
opportunities. They were, however, beaten at the game by Sergeant
Durrans, 6th L.F., who on June 14 crept 450 yards into the long
grass in No Man’s Land and patiently bided his time. When the
snipers disclosed their positions by firing he gave a fine display of
marksmanship for two and a half hours and picked off half a dozen
of them. He was wounded in the right knee.
On the night of June 12 an officer of the 5th Manchesters, who
were then holding the “Slag Heap,” was detailed to reconnoitre
Wigan Copse, in No Man’s Land, examine the wire—concealed by
the long grass—and find the gaps. He led a party of six men to the
copse, but could find no gaps, the wire being apparently uninjured.
He crawled round it to the back of the copse, and eventually
discovered an opening through which he crept, accompanied by a
corporal, the rest of the party being posted outside. A narrow trench
and some rough shelters were located, but there was no sign of life
until the officer, desiring to take back a souvenir of his visit, disturbed
a pile of stick-bombs. A tarpaulin then moved and a voice challenged
them. The officer fired several shots with his revolver, and yells
indicated that at least one of the Germans had been hit. The fire was
returned, and in a moment the wood seemed alive with the enemy.
As the exit was too close to the German front line for comfort the
patrol crept away and lay in the long grass until the noise died down,
when they withdrew untouched. On the following afternoon the
enemy guns registered on the copse, and in the evening bombarded
the British line and put down a box-barrage, under cover of which a
company of the enemy charged the copse, yelling “Hands up, the
English!” They suffered severely from rifle and Lewis-gun fire.
Information was obtained later from prisoners that the garrison of the
copse had been so scared by the sudden appearance of Englishmen
in the wood that they had bolted, and had reported that the British
were in possession of the post. Hence the elaborate counter-attack
of the empty copse.
In the afternoon of June 22 a particularly daring raid was carried
out by Sergeant J. Sugden (later Lieutenant) of the 10th
Manchesters. Annoyance had been caused by a small trench-mortar,
and as it was suspected that this was fired from a derelict elephant
hut a few hundred yards from our most forward post, Sugden—a
born scout—resolved to make sure. He found that there was a sentry
guarding a dug-out near to the elephant hut, and that the man
seemed inclined to take his duties easily. Returning, he chose two
companions, whom he posted on a flank, while he crawled
unobserved to within a few yards of the dug-out. He then quietly
informed the sentry, in fluent German, that he was covered, and that
he would be shot if he showed the slightest hesitation in obeying
orders. He showed none, so Sugden ordered the other occupants of
the post to come out with their hands up. At first they seemed
inclined to dispute the matter, until told that they were surrounded
and that unless they obeyed promptly they would quickly find
themselves blown into a region even lower than their dugout. The
threat had its effect; they meekly obeyed, and Sugden had the
satisfaction of bringing four very sullen Germans, carrying a trench-
mortar, across No Man’s Land in broad daylight. The Corps
Commander sent a complimentary letter to the Battalion Commander
praising the initiative and the aggressive tactics of his men, and
congratulated Sugden personally, and also gave him special leave
for fourteen days.
At the end of June the 7th Manchesters were Night Patrols and
instructed to supply a party to raid Wigan Copse Raids
and bring back three prisoners. Lieutenant A.
Hodge (later Lieut.-Colonel, commanding 1/8th Manchesters), who
was chosen to carry out the raid, gave his men some realistic
preliminary training. At 11 p.m. on July 3 the guns opened on the
enemy’s lines behind the copse, and Hodge’s platoon, after a crawl
of more than half an hour, rushed the copse. Its occupants tried to
bolt, but the box-barrage hemmed them in and they had to choose
between fighting and surrender. One young German, who had been
lying in the grass on outpost duty, was so scared that in his fright he
rose and attached himself to the Manchesters, until Hodge took him
by the scruff of the neck and flung him to the man behind. But no
one wanted the Boche, so he was flung from one to another until
finally one of the covering party held him captive. After five minutes’
rough-and-tumble, in which none of the 7th was hurt, though a
number of the enemy had been bayonetted, or shot by the officer’s
revolver, Hodge returned with the three prisoners indented for. It had
been a model raid.
On the 8th of July the Division was relieved by the 58th Division,
with the exception of the artillery, which remained in the line with the
58th Division, and later with the 9th Division, at Havrincourt Wood
until the end of August, when they rejoined their own Division in
Belgium. The artillery’s periods of “rest” were infrequent and
uncertain. Whenever the divisional infantry was relieved the guns
would remain in the line for a time, attached to the relieving Division.
From the artillery point of view the work at Havrincourt consisted
mainly of concentrated fire at night on back areas of the enemy line
and in artillery duels. Corporal Charles Gee, “B” Battery, 210th
Brigade, twice won distinction during this period. On July 22, near
Hermies, a hostile shell set a gun-pit on fire, and Gee, with
Bombardier W. Pate, disregarding the explosions, succeeded in
covering the burning material with earth, and so saved a
considerable amount of ammunition. On August 13, during a heavy
bombardment of the battery position, a shell burst in a dugout
occupied by one man, blowing off one of his legs. Accompanied by
Gunner W. Armitstead, Gee went to the injured man’s assistance,
and while they were removing the debris a shell burst near and
knocked both over. They managed to extricate the man, bandage his
wounds, and convey him to safety, being all the time under heavy
fire and suffering from fumes.
The Ytres sector was looked back upon as a “bon” front by
comparison with others with which acquaintance was made later.
Here the Divisional Concert Party, which afterwards achieved fame
under the title of “Th’ Lads,” was first organized. “Th’ Lads” soon
became a feature which the Division could ill have spared, and the
delightful entertainments given under the fine trees of Little Wood
are recalled with genuine pleasure.
From July 9 to August 22 the Divisional Headquarters were in the
Third Army reserve area at Achiet-le-Petit, where the 127th Brigade
was stationed, with the 125th Brigade at Gomiecourt and the 126th
at Courcelles. This area, which was visited by the King on July 12-
13, had been wholly devastated. What had once been a village was
now a heap of broken bricks and rubble; a few stark walls standing
grimly against the skyline and a name painted in bold black lettering
on a white ground informed the passer-by what village had once
stood here. The fields were scarred with trenches and shell holes,
and all the indescribable debris of an abandoned battlefield was
spread around. Most of the troops were under canvas, but as there
were not enough tents for all a number had to live in little “shacks”
made of odd bits of corrugated iron and any other scrap material
available. The fine weather continued and the six weeks in this area
partook of the nature of a holiday, though the days were fully taken
up by intensive training, special attention being paid to training in
attacks upon fortified posts and strong points. Instructional visits
were made to the scarred battlefields of the Somme, Brig.-General
Henley taking a number of his officers to Thiepval and giving his
personal experiences of the fighting there. The various training
stunts—battalion, brigade, and divisional—enabled the troops to gain
a thorough knowledge of the ground in this area, and this familiarity
with the topography stood them in good stead when seven months
later they were called upon to withstand the German onrush on this
very ground. Time was found for divisional and brigade sports, inter-
battalion football and cricket matches, boxing contests in the large
crater at Achiet-le-Petit; and the visits of “Th’ Lads” to the Brigade
Headquarters were keenly appreciated. There had never been such
a time for sports as this, and it was hard to realize that “there was a
war on.” Newly-painted vehicles, perfectly turned-out animals, bands
playing, troops spick-and-span, all combined to lend a gala aspect to
this period.
On August 22 the period of rest came to an end, and the Division
entrained for the most detested of all fronts—Ypres.