Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
Open accessAbstract
As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people»s moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person»s assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people»s unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people»s fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.
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"I'm not sure what difference is between their content and mine, other than the person itself"
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- Authors
- Grgic-Hlaca, N., Redmiles, E. M., Gummadi, K. P., & Weller, A.
- Affiliations
- Max Planck Institute for Software Systems (MPI-SWS) & Saarland University, Saarbrücken, Germany
- University of Maryland, College Park (US)
- Max Planck Institute for Software Systems (DE)
- University of Cambridge (GB)
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 903-912
- OpenAlex
- W2964316623
- Cited by
- 207
- References
- 54
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 26 · 2023: 25 · 2022: 35 · 2021: 39 · 2020: 20 · 2019: 23 · 2018: 4
- FWCI
- 38.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Funders
- National Science Foundation Graduate Research Fellowship Program (DGE 1322106)
- Leverhulme Trust via the CFI
- The Alan Turing Institute under EPSRC grant (EP/N510129/1 & TU/B/000074)
- David MacKay Newton research fellowship at Darwin College
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Keywords
- algorithmic fairness, perceived fairness, algorithmic decision-making, feature relevance, volitionality, reliability, moral judgment, fairness judgments
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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