extended-abstract

A Case Study of Integrating Fairness Visualization Tools in Machine Learning Education

Authors:

Annuska Zolyomi,

Jay QuedadoAuthors Info & Claims

CHI EA '22: CHI Conference on Human Factors in Computing Systems Extended Abstracts

Article No.: 37, Pages 1 - 7

https://doi.org/10.1145/3491101.3503568

Published: 28 April 2022 Publication History

Abstract

As demonstrated by media attention and research, Artificial Intelligence systems are not adequately addressing issues of fairness and bias, and more education on these topics is needed in industry and higher education. Currently, computer science courses that cover AI fairness and bias focus on statistical analysis or, on the other hand, attempt to bring in philosophical perspectives that lack actionable takeaways for students. Based on long-standing pedagogical research demonstrating the importance of using tools and visualizations to reinforce student learning, this case study reports on the impacts of using publicly-available visualization tools used in HCI practice as a resource for students examining algorithmic fairness concepts. Through qualitative review and observations of four focus groups, we examined six open-source fairness tools that enable students to visualize, quantify and explore algorithmic biases. The findings of this study provide insights into the benefits, challenges, and opportunities of integrating fairness tools as part of machine learning education.

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Wongvorachan TBulut OLiu JMazzullo E(2024)A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine LearningInformation10.3390/info1506032615:6(326)Online publication date: 4-Jun-2024
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cover image ACM Conferences

CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

3066 pages

ISBN:9781450391566

DOI:10.1145/3491101

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA

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Published: 28 April 2022

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Cited By

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Hutson JFulcher BRatican J(2024)Enhancing Assessment and Feedback in Game Design ProgramsIJERI: International Journal of Educational Research and Innovation10.46661/ijeri.11038(1-20)Online publication date: 3-Dec-2024
https://doi.org/10.46661/ijeri.11038
Wongvorachan TBulut OLiu JMazzullo E(2024)A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine LearningInformation10.3390/info1506032615:6(326)Online publication date: 4-Jun-2024
https://doi.org/10.3390/info15060326
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
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Yan XZhou YMishra AMishra HWang B(2024)Exploring Visualization for Fairness in AI Education2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00010(1-10)Online publication date: 23-Apr-2024
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Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
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Balayn AYurrita MYang JGadiraju U(2023)“☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic HarmsProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604674(482-495)Online publication date: 8-Aug-2023
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