# Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

arXiv: https://arxiv.org/abs/1802.09548 · HTML: https://mlsanity.com/paper/1802.09548/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 30 of 35)

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- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2209.11812/) (2022)
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- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [Marrying Fairness and Explainability in Supervised Learning](https://mlsanity.com/paper/2204.02947/) (2022)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [AI Risk Management Should Incorporate Both Safety and Security](https://mlsanity.com/paper/2405.19524/) (2024)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
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- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [The Algorithmic Imprint](https://mlsanity.com/paper/2206.03275/) (2022)
- [Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems](https://mlsanity.com/paper/2108.06500/) (2021)
- [Time Can Invalidate Algorithmic Recourse](https://mlsanity.com/paper/2410.08007/) (2024)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025)
- [Fast Feature Selection with Fairness Constraints](https://mlsanity.com/paper/2202.13718/) (2022)
- [On the Relationship Between Explanations, Fairness Perceptions, and Decisions](https://mlsanity.com/paper/2204.13156/) (2022)
- [Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways](https://mlsanity.com/paper/2402.04420/) (2024)
- [Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty](https://mlsanity.com/paper/2205.06483/) (2022)
- [Learning Social Fairness Preferences from Non-Expert Stakeholder Opinions in Kidney Placement](https://mlsanity.com/paper/2404.03800/) (2024)
- [Fair for a few: Improving Fairness in Doubly Imbalanced Datasets](https://mlsanity.com/paper/2506.14306/) (2025)
- [Descriptive AI Ethics: Collecting and Understanding the Public Opinion](https://mlsanity.com/paper/2101.05957/) (2021)
- [Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness Notions](https://mlsanity.com/paper/2107.01277/) (2021)
- [Fairness of Machine Learning Algorithms in Demography](https://mlsanity.com/paper/2202.01013/) (2022)
- [On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers](https://mlsanity.com/paper/2202.08250/) (2022)
- [Assessing Perceived Fairness from Machine Learning Developer's Perspective](https://mlsanity.com/paper/2304.03745/) (2023)

## Abstract (arXiv)

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.