Explaining models: an empirical study of how explanations impact fairness judgment
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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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Dodge et al. found that local explanations, by highlighting unfair features used for individual predictions, appear to be more alarming than global explanations when used to explain an unfair model's decisions, and thus more effective in calibrating people's fairness judgment of ML models.
“They found that local explanations, by highlighting unfair features used for individual predictions, appear to be more alarming than global explanations when used to explain an unfair model's decisions, and thus more effective in calibrating people's fairness judgment of ML models.”
From Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020) · cites this paper as [8]
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Experimental — accuracy not yet measuredTasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval, recidivism prediction, house price estimation, and student admission.
“Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval [27, 34, 124], recidivism prediction [31, 47, 86], house price estimation [2, 13, 25], and student admission [13, 24].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [31]
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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)
- Venue
- Proceedings of the 24th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2019-03-17
- Pages
- 275-285
- 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
- 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)
- Social Sciences › Safety Research › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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