# Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms

arXiv: https://arxiv.org/abs/2505.07339 · HTML: https://mlsanity.com/paper/2505.07339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 124 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 24 of 24)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems](https://mlsanity.com/paper/2102.01196/) (2021)
- [Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches](https://mlsanity.com/paper/2205.04460/) (2022)
- [Affirmative Algorithms: The Legal Grounds for Fairness as Awareness](https://mlsanity.com/paper/2012.14285/) (2020)
- [Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy](https://mlsanity.com/paper/2206.00945/) (2022)
- [Dimensions of Diversity in Human Perceptions of Algorithmic Fairness](https://mlsanity.com/paper/2005.00808/) (2020)
- [AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI](https://mlsanity.com/paper/2106.00326/) (2021)
- [The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action](https://mlsanity.com/paper/2404.12143/) (2024)
- [Demographics and discussion influence views on algorithmic fairness](https://mlsanity.com/paper/1712.09124/) (2017)
- [Exploring User Opinions of Fairness in Recommender Systems](https://mlsanity.com/paper/2003.06461/) (2020)
- [Structural Interventions and the Dynamics of Inequality](https://mlsanity.com/paper/2406.01323/) (2024)
- [Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability](https://mlsanity.com/paper/2407.11199/) (2024)
- [Who Gets What, According to Whom? An Analysis of Fairness Perceptions in Service Allocation](https://mlsanity.com/paper/2105.04452/) (2021)

## Abstract (arXiv)

Affirmative algorithms have emerged as a potential answer to algorithmic discrimination, seeking to redress past harms and rectify the source of historical injustices. We present the results of two experiments (N=1193) capturing laypeople's perceptions of affirmative algorithms -- those which explicitly prioritize the historically marginalized -- in hiring and criminal justice. We contrast these opinions about affirmative algorithms with folk attitudes towards algorithms that prioritize the privileged (i.e., discriminatory) and systems that make decisions independently of demographic groups (i.e., fair). We find that people -- regardless of their political leaning and identity -- view fair algorithms favorably and denounce discriminatory systems. In contrast, we identify disagreements concerning affirmative algorithms: liberals and racial minorities rate affirmative systems as positively as their fair counterparts, whereas conservatives and those from the dominant racial group evaluate affirmative algorithms as negatively as discriminatory systems. We identify a source of these divisions: people have varying beliefs about who (if anyone) is marginalized, shaping their views of affirmative algorithms. We discuss the possibility of bridging these disagreements to bring people together towards affirmative algorithms.