research-article

Understanding Algorithms through Exploration: Supporting Knowledge Acquisition in Primary Tasks

Authors:

Charlotte Anlauff,

Tim Ordenewitz,

Heinrich HussmannAuthors Info & Claims

MuC '19: Proceedings of Mensch und Computer 2019

Pages 127 - 136

https://doi.org/10.1145/3340764.3340772

Published: 08 September 2019 Publication History

Abstract

We investigate exploration as an alternative to explanation to improve user understanding of algorithms and algorithmic decision-making. Drawing on complex problem-solving as defined in cognitive science, we conducted a think-aloud study in the lab (N=10) as well as an MTurk online study (N=123) using a flight booking scenario to see if and how exploration supports knowledge acquisition in two different tasks. One group was told to focus on booking the cheapest flight (knowledge acquisition as a secondary task), the other on understanding the system logic (knowledge acquisition as a primary task).

Our results indicate that exploration, even as a secondary task, may contribute to knowledge about the underlying algorithm. However, our study also suggests that the overall knowledge acquired through exploration is limited in the sense that it gives people an idea of how a system works, rather than teaching them concrete rules they can recall.

Overall, we conclude that exploration presents a design opportunity to interweave knowledge acquisition with users' primary task, and may thus contribute to (but not substitute) existing design solutions for supporting users in understanding algorithmic decision-making.

References

[1]

Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 582, 18 pages.

Digital Library

[2]

Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (dec 2014), 105.

Digital Library

[3]

Matt Lease An Nguyen, Byron Wallace. 2019. Mash: Software Tools for Developing Interactive and Transparent Machine Learning Systems. In 2nd Workshop on Explainable Smart Systems at the ACM Conference on Intelligent User Interfaces (IUI '19).

[4]

Alan F. Blackwell. 2015. Interacting with an Inferred World: The Challenge of Machine Learning for Humane Computer Interaction. In Proceedings of The Fifth Decennial Aarhus Conference on Critical Alternatives (CA '15). Aarhus University Press, 169--180.

Digital Library

[5]

Virginia Braun and Victoria Clarke. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology 3, 2 (2006), 77--101.

[6]

Axel Buchner. 1995. Basic Topics and Approaches to the Study of Complex Problem Solving. Complex Problem Solving: The European Perspective (1995), 27--63.

[7]

Tom Chitty. 2018. This is how Airlines Price Tickets. https://www.cnbc.com/2018/08/03/how-do-airlines-price-seat-tickets.html. Accessed 29 January 2019.

[8]

Gabriella Cortellessa, Vittoria Giuliani, Massimiliano Scopelliti, and Amedeo Cesta. 2005. Key Issues in Interactive Problem Solving: An Empirical Investigation on Users Attitude. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Digital Library

[9]

Henriette Cramer, Vanessa Evers, Satyan Ramlal, Maarten Van Someren, Lloyd Rutledge, Natalia Stash, Lora Aroyo, and Bob Wielinga. 2008. The Effects of Transparency on Trust in and Acceptance of a Content-based Art Recommender. User Modeling and User-Adapted Interaction (2008).

Digital Library

[10]

Piotr Dabkowski and Yarin Gal. 2017. Real Time Image Saliency for Black Box Classifiers. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., USA, 6970--6979. http://dl.acm.org/citation.cfm?id=3295222.3295440

Digital Library

[11]

Anupam Das, Joseph Bonneau, Matthew Caesar, Nikita Borisov, and XiaoFeng Wang. 2014. The Tangled Web of Password Reuse. In NDSS'14, Vol. 14. San Diego, CA, USA, 23--26.

[12]

Sjaak de Mul and Herre van Oostendorp. 1996. Learning User Interfaces by Exploration. Acta Psychologica 91, 3 (1996), 325--344.

[13]

Nicholas Diakopoulos. 2014. Algorithmic Accountability Reporting: On the Investigation of Black Boxes. Tow Center for Digital Journalism Publications (2014), 1--33.

[14]

Stephen W. Draper and Stephen B. Barton. 1993. Learning by Exploration and Affordance Bugs. In INTERACT '93 and CHI '93 Conference Companion on Human Factors in Computing Systems (CHI '93). ACM, New York, NY, USA, 75--76.

Digital Library

[15]

John J. Dudley and Per Ola Kristensson. 2018. A Review of User Interface Design for Interactive Machine Learning. ACM Trans. Interact. Intell. Syst. 8, 2, Article 8 (June 2018), 37 pages.

Digital Library

[16]

Malin Eiband, Hanna Schneider, and Daniel Buschek. 2018. Normative vs Pragmatic: Two Perspectives on the Design of Explanations in Intelligent Systems. In 1st Workshop on Explainable Smart Systems at the ACM Conference on Intelligent User Interfaces (IUI '18).

[17]

Malin Eiband, Sarah Theres Völkel, Daniel Buschek, Sophia Cook, and Heinrich Hussmann. 2019. When People and Algorithms Meet: User-reported Problems in Intelligent Everyday Applications. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI '19). ACM, New York, NY, USA, 96--106.

Digital Library

[18]

Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I "Like" It, then I Hide It: Folk Theories of Social Feeds. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 2371--2382.

Digital Library

[19]

Andreas Fischer, Samuel Greiff, and Joachim Funke. 2012. The Process of Solving Complex Problems. The Journal of Problem Solving 4, 1 (2012).

[20]

Ruth Fong and Andrea Vedaldi. 2017. Interpretable Explanations of Black Boxes by Meaningful Perturbation. CoRR abs/1704.03296 (2017). arXiv:1704.03296 http://arxiv.org/abs/1704.03296

[21]

Peter A. Frensch and Joachim Funke. 1995. Definitions, Traditions, and a General Framework for Understanding Complex Problem Solving. In Complex Problem Solving - The European Perspective.

[22]

Samuel Greiff, Sascha Wüstenberg, and Joachim Funke. 2012. Dynamic Problem Solving: A New Assessment Perspective. Applied Psychological Measurement 36, 3 (2012), 189--213.

[23]

Kevin Hamilton, Karrie Karahalios, Christian Sandvig, and Motahhare Eslami. 2014. A Path to Understanding the Effects of Algorithm Awareness. In CHI '14 Extended Abstracts on Human Factors in Computing Systems (CHI EA '14). ACM, New York, NY, USA, 631--642.

Digital Library

[24]

Lucas D. Introna. 2016. Algorithms, Governance, and Governmentality: On Governing Academic Writing. Science, Technology, & Human Values 41, 1 (2016), 17--49.

[25]

Hilary Johnson and Peter Johnson. 1993. Explanation Facilities and Interactive Systems. In Proceedings of the 1st International Conference on Intelligent User Interfaces (IUI '93). ACM, New York, NY, USA, 159--166.

Digital Library

[26]

Jose H. Kerstholt, Peter O. Passenier, Kees Houttuin, and Herke Schuffel. 1996. The Effect of a Priori Probability and Complexity on Decision Making in a Supervisory Control Task. Human Factors: The Journal of the Human Factors and Ergonomics Society (1996).

[27]

Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng Keen Wong. 2013. Too Much, too Little, or just Right? Ways Explanations Impact End Users' Mental Models. In Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC.

[28]

Pierre Le Bras, David A. Robb, Thomas S. Methven, Stefano Padilla, and Mike J. Chantler. 2018. Improving User Confidence in Concept Maps: Exploring Data Driven Explanations. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 404, 13 pages.

Digital Library

[29]

J. D. Lee and K. A. See. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society 46, 1 (2004), 50--80.

[30]

Brian Y. Lim and Anind K. Dey. 2009. Assessing Demand for Intelligibility in Context-aware Applications. In Proceedings of the 11th International Conference on Ubiquitous Computing (UbiComp '09). ACM, New York, NY, USA, 195--204.

Digital Library

[31]

Brian Y. Lim and Anind K. Dey. 2011. Design of an Intelligible Mobile Context-aware Application. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (Mobile HCI '11).

Digital Library

[32]

Brian Y. Lim, Anind K. Dey, and Daniel Avrahami. 2009. Why and Why Not Explanations Improve the Intelligibility of Context-aware Intelligent Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 09). ACM, New York, NY, USA, 2119--2128.

Digital Library

[33]

Zachary C. Lipton. 2016. The Mythos of Model Interpretability. (June 2016).

Digital Library

[34]

Sarah Theres Völkel Daniel Buschek Michael Chromik, Malin Eiband. 2019. Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems. In 2nd Workshop on Explainable Smart Systems at the ACM Conference on Intelligent User Interfaces (IUI '19).

[35]

Neville Moray. 1987. Intelligent Aids, Mental Models, and the Theory of Machines. International Journal of Man-Machine Studies (1987).

Digital Library

[36]

Hugh Morris. 2015. Never Book Flights on Fridays -- and Eight other Tips for Getting Cheap Air Fares. https://www.telegraph.co.uk/travel/news/Never-book-flights-on-Fridays-and-eight-other-tips-for-getting-cheap-air-fares/. Accessed 29 January 2019.

[37]

Mouseflow, ApS. 2018. Session Replay, Heatmaps, Funnels, Forms & User Feedback. Retrieved August 7, 2018 from https://mouseflow.com/

[38]

Bonnie M. Muir. 1994. Trust in Automation: Part I. Theoretical Issues in the Study of Trust and Human Intervention in Automated Systems. Ergonomics 37, 11 (1994), 1905--1922.

[39]

Brad Myers, David A. Weitzman, Andrew J. Ko, and Duen Horng Chau. 2006. Answering Why and Why Not Questions in User Interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '06) (2006).

Digital Library

[40]

Donald A. Norman. 1987. Human-computer Interaction. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, Chapter Some Observations on Mental Models, 241--244. http://dl.acm.org/citation.cfm?id=58076.58097

Digital Library

[41]

Emilee Rader, Kelley Cotter, and Janghee Cho. 2018. Explanations As Mechanisms for Supporting Algorithmic Transparency. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 103, 13 pages.

Digital Library

[42]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1135--1144.

Digital Library

[43]

Advait Sarkar. 2015. Confidence, Command, Complexity: Metamodels for Structured Interaction with Machine Intelligence. In Proceedings of the 26th Annual Workshop of the Psychology of Programming Interest Group. Psychology of Programming Interest Group, Bournemouth, UK, 23--36.

[44]

Wolfgang Schoppek. 2002. Examples, Rules, and Strategies in the Control of Dynamic Systems. Cognitive Science Quarterly 2, 1 (2002), 63--92.

[45]

Nava Tintarev and Judith Masthoff. 2007. A Survey of Explanations in Recommender Systems. In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 801--810.

Digital Library

[46]

Judith E. Tschirgi. 1980. Sensible Reasoning: A Hypothesis about Hypotheses. Child Development 51, 1 (1980), 1--10.

[47]

Joe Tullio, Anind K. Dey, Jason Chalecki, and James Fogarty. 2007. How It Works: A Field Study of Non-technical Users Interacting with an Intelligent System. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '07). ACM, New York, NY, USA, 31--40.

Digital Library

[48]

Herre Van Oostendorp and Sjaak De Mul. 1999. Learning by Exploration: Thinking Aloud while Exploring an Information System. Instructional Science 27, 3 (July 1999), 269--284.

[49]

Ka-Ping Yee. 2004. Aligning Security and Usability. IEEE Security Privacy 2, 5 (September 2004), 48--55.

Digital Library

Cited By

Kim HLim Y(2023)Investigating How Users Design Everyday Intelligent Systems in UseProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596039(702-711)Online publication date: 10-Jul-2023
https://doi.org/10.1145/3563657.3596039
Schrills TGruner MPeuscher HFranke T(2023)Safe Environments to Understand Medical AI - Designing a Diabetes Simulation Interface for Users of Automated Insulin DeliveryDigital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management10.1007/978-3-031-35748-0_23(306-328)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35748-0_23
Ahmed SBalasubramanian HStumpf SMorrison CSellen AGrayson MPaternò FOliver NConati CSpano LTintarev N(2020)Investigating the intelligibility of a computer vision system for blind usersProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377508(419-429)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377508

Index Terms

Understanding Algorithms through Exploration: Supporting Knowledge Acquisition in Primary Tasks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Automated Comprehension Tasks in Software Exploration
ASE '09: Proceedings of the 24th IEEE/ACM International Conference on Automated Software Engineering

Finding issues in software usually requires a serie of comprehension tasks. After every task, an engineer explores the results and decides whether further tasks are required. Software comprehension therefore is a combination of tasks and a supported ...
Random Methods for Fast Exploration of the Raw Video Material
DEXA '09: Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application

In this work we propose a visualization and access method for exploring a database of raw video material, where the span of information stored can be huge and difficult to understand at a glance. Developed visual mining tool presents the overview of the ...
Accentuating visualization parameters to guide exploration
CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

We present a new method for displaying visualization parameters to guide casual data exploration. When visualizing datasets with large parameter spaces it can be difficult to move between data views. Building on social navigation and degree-of-interest ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

MuC '19: Proceedings of Mensch und Computer 2019

September 2019

863 pages

ISBN:9781450371988

DOI:10.1145/3340764

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MuC'19

MuC'19: Mensch-und-Computer

September 8 - 11, 2019

Hamburg, Germany

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
139
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kim HLim Y(2023)Investigating How Users Design Everyday Intelligent Systems in UseProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596039(702-711)Online publication date: 10-Jul-2023
https://doi.org/10.1145/3563657.3596039
Schrills TGruner MPeuscher HFranke T(2023)Safe Environments to Understand Medical AI - Designing a Diabetes Simulation Interface for Users of Automated Insulin DeliveryDigital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management10.1007/978-3-031-35748-0_23(306-328)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35748-0_23
Ahmed SBalasubramanian HStumpf SMorrison CSellen AGrayson MPaternò FOliver NConati CSpano LTintarev N(2020)Investigating the intelligibility of a computer vision system for blind usersProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377508(419-429)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377508

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents