Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

要旨

Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.

受賞
Best Paper
キーワード
AI
ML
ethics
fairness
co-design
checklists
著者
Michael A. Madaio
Carnegie Mellon University, Pittsburgh, PA, USA
Luke Stark
Microsoft Research, Montreal, PQ, Canada
Jennifer Wortman Vaughan
Microsoft Research, New York, NY, USA
Hanna Wallach
Microsoft Research, New York City, NY, USA
DOI

10.1145/3313831.3376445

論文URL

https://doi.org/10.1145/3313831.3376445

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: In dialogue with AI

Paper session
316C MAUI
5 件の発表
2020-04-29 01:00:00
2020-04-29 02:15:00
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