Cited by 287Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AISoftware Testing and Debugging Techniques

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Explaining models: an empirical study of how explanations impact fairness judgment

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Dodge, Jonathan, Liao, Q. Vera, Zhang, Yunfeng, Bellamy, Rachel K. E., Dugan, Casey·2019·Proceedings of the 24th International Conference on Intelligent User Interfaces

DOI 10.1145/3301275.3302310

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Abstract

Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.

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“However, this growing body of research is criticized for łapproaching this [XAI] challenge in a vacuum considering only the computational problemsž [27] without the quintessential understanding of how people perceive and use the explanations.”
Background
Explanation in artificial intelligence: Insights from the social sciences
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Authors: Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C.
Affiliations: Oregon State University (US)
IBM (United States) (US)
ORCID: Dodge, Jonathan
Liao, Q. Vera
Zhang, Yunfeng
Bellamy, Rachel K. E.
Dugan, Casey
Venue: Proceedings of the 24th International Conference on Intelligent User Interfaces
Publisher: ACM
Published: 2019-03-17
Pages: 275-285
DOI: 10.1145/3301275.3302310
OpenAlex: W4288414189
Cited by: 286
References: 43
Citations by year: 2026: 24 · 2025: 44 · 2024: 52 · 2023: 48 · 2022: 56 · 2021: 41 · 2020: 18 · 2019: 3
FWCI: 11.04
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Army Research Laboratory and UK Ministry of Defence (W911NF-16-3-0001)
Defense Advanced Research Projects Agency (N66001-17-2-4030)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords: explainable AI, algorithmic fairness, fairness judgments, human-in-the-loop, explanation styles, individual differences, personalized explanations, adaptive explanations, trust in machine learning

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Explaining models: an empirical study of how explanations impact fairness judgment

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