course

Introduction to Explainable AI

Authors:

Q. Vera Liao,

Moninder Singh,

Yunfeng Zhang,

Rachel K.E. BellamyAuthors Info & Claims

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 4

https://doi.org/10.1145/3334480.3375044

Published: 25 April 2020 Publication History

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Abstract

As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations that allow users to understand the AI has become a ubiquitous concern in human-AI interaction. Recently, a number of open-source toolkits are making the growing collection of of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic of XAI, aiming to provide an overview on the trends and methods of XAI and also help attendees gain hands-on experience of creating different styles of explanation with an XAI toolkit.

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. ACM, 582.

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Google Scholar

[2]

Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilovi´ c, and others. 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arXiv preprint arXiv:1909.03012 (2019).

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Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso. 2019. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics 8, 8 (2019), 832.

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[4]

Jonathan Dodge, Q Vera Liao, Yunfeng Zhang, Rachel KE Bellamy, and Casey Dugan. 2019. Explaining models: an empirical study of how explanations impact fairness judgment. In Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM, 275--285.

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FICO. 2018. FICO Explainable Machine Learning Challenge. https://community.fico.com/s/ explainable-machine-learning-challenge. (2018). Last accessed 2019-08.

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Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2019. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5 (2019), 93.

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Cited By

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Gamboa L(2025)AI-Boosted Decision Techniques for Strategy Formulation and ImplementationDecision Sciences10.1007/978-3-031-78238-1_5(50-59)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-3-031-78238-1_5
Nallakaruppan MChaturvedi HGrover VBalusamy BJaraut PBahadur JMeena VHameed I(2024)Credit Risk Assessment and Financial Decision Support Using Explainable Artificial IntelligenceRisks10.3390/risks1210016412:10(164)Online publication date: 15-Oct-2024
https://doi.org/10.3390/risks12100164
Lowe MQin RMao X(2022)A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and MonitoringWater10.3390/w1409138414:9(1384)Online publication date: 24-Apr-2022
https://doi.org/10.3390/w14091384
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Index Terms

Introduction to Explainable AI
1. Computing methodologies
  1. Machine learning
2. Human-centered computing

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Information & Contributors

Information

Published In

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

4474 pages

ISBN:9781450368193

DOI:10.1145/3334480

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2020

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CHI '20

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SIGCHI

CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

View all

Gamboa L(2025)AI-Boosted Decision Techniques for Strategy Formulation and ImplementationDecision Sciences10.1007/978-3-031-78238-1_5(50-59)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-3-031-78238-1_5
Nallakaruppan MChaturvedi HGrover VBalusamy BJaraut PBahadur JMeena VHameed I(2024)Credit Risk Assessment and Financial Decision Support Using Explainable Artificial IntelligenceRisks10.3390/risks1210016412:10(164)Online publication date: 15-Oct-2024
https://doi.org/10.3390/risks12100164
Lowe MQin RMao X(2022)A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and MonitoringWater10.3390/w1409138414:9(1384)Online publication date: 24-Apr-2022
https://doi.org/10.3390/w14091384
Estivill-Castro VGilmore EHexel R(2022)Constructing Explainable Classifiers from the Start—Enabling Human-in-the Loop Machine LearningInformation10.3390/info1310046413:10(464)Online publication date: 29-Sep-2022
https://doi.org/10.3390/info13100464
Ajenaghughrure ISousa SLamas D(2021)Psychophysiological Modeling of Trust In TechnologyProceedings of the ACM on Human-Computer Interaction10.1145/34597455:EICS(1-25)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3459745
Salminen JCorporan JJung SJansen B(2021)Taking Back Control of Social Media Feeds with Take Back Control2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)10.1109/INISTA52262.2021.9548614(1-7)Online publication date: 25-Aug-2021
https://doi.org/10.1109/INISTA52262.2021.9548614
Alloatti FBosca ADi Caro LPieraccini F(2021)Diabetes and conversational agents: the AIDA project case studyDiscover Artificial Intelligence10.1007/s44163-021-00005-11:1Online publication date: 22-Sep-2021
https://doi.org/10.1007/s44163-021-00005-1
Abd-Alrazaq ASchneider JAlhuwail DHamdi MAl-Kuwari SAl-Thani DHouseh M(2021)Artificial Intelligence in the Fight Against the COVID-19 Pandemic: Opportunities and ChallengesMultiple Perspectives on Artificial Intelligence in Healthcare10.1007/978-3-030-67303-1_15(185-196)Online publication date: 6-Aug-2021
https://doi.org/10.1007/978-3-030-67303-1_15
Estivill-Castro VGilmore EHexel R(2020)Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283240(3852-3859)Online publication date: 11-Oct-2020
https://dl.acm.org/doi/10.1109/SMC42975.2020.9283240

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Abstract

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Questioning the AI: Informing Design Practices for Explainable AI User Experiences