research-article

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Authors:

Michael Madaio,

Hariharan Subramonyam,

Jennifer Wortman Vaughan,

Hanna WallachAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW1

Article No.: 52, Pages 1 - 26

https://doi.org/10.1145/3512899

Published: 07 April 2022 Publication History

Abstract

Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior research has highlighted gaps between the intended design of these tools and practices and their use within particular contexts, including gaps caused by the role that organizational factors play in shaping fairness work. In this paper, we investigate these gaps for one such practice: disaggregated evaluations of AI systems, intended to uncover performance disparities between demographic groups. By conducting semi-structured interviews and structured workshops with thirty-three AI practitioners from ten teams at three technology companies, we identify practitioners' processes, challenges, and needs for support when designing disaggregated evaluations. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups on which to focus, and collecting datasets with which to conduct disaggregated evaluations. More generally, we identify impacts on fairness work stemming from a lack of engagement with direct stakeholders or domain experts, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.

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The supplemental material for our paper includes the protocols we used for the semi-structured interviews and the planning workshops.

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Index Terms

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
    2. Empirical studies in collaborative and social computing

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Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW1

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April 2022

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EISSN:2573-0142

DOI:10.1145/3530837

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Blandin JKash I(2024)Learning Fairness from Demonstrations via Inverse Reinforcement LearningProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658539(51-61)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658539
Kommiya Mothilal RGuha SAhmed S(2024)Towards a Non-Ideal Methodological Framework for Responsible MLProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642501(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642501
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