# Dimensions of Diversity in Human Perceptions of Algorithmic Fairness

arXiv: https://arxiv.org/abs/2005.00808 · HTML: https://mlsanity.com/paper/2005.00808/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 13 of 13)

- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- ["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)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm](https://mlsanity.com/paper/2304.02176/) (2023)
- [Pitfalls of Explainable ML: An Industry Perspective](https://mlsanity.com/paper/2106.07758/) (2021)
- [On the Relationship Between Explanations, Fairness Perceptions, and Decisions](https://mlsanity.com/paper/2204.13156/) (2022)
- [Beyond traditional assumptions in fair machine learning](https://mlsanity.com/paper/2101.12476/) (2021)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements](https://mlsanity.com/paper/2306.10043/) (2023)
- [Fair Enough? A map of the current limitations of the requirements to have fair algorithms](https://mlsanity.com/paper/2311.12435/) (2023)
- [Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms](https://mlsanity.com/paper/2505.07339/) (2025)

## Abstract (arXiv)

A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people's lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how individual factors like sociodemographics or direct experience with a decision-making scenario may affect their ethical views. We take a step toward filling this gap by exploring how people's perceptions of one aspect of procedural algorithmic fairness (the fairness of using particular features in an algorithmic decision) relate to their (i) demographics (age, education, gender, race, political views) and (ii) personal experiences with the algorithmic decision-making scenario. We find that political views and personal experience with the algorithmic decision context significantly influence perceptions about the fairness of using different features for bail decision-making. Drawing on our results, we discuss the implications for stakeholder engagement and algorithmic oversight including the need to consider multiple dimensions of diversity in composing oversight and regulatory bodies.