Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms
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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.
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.
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References · 24
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2017Counterfactual Fairnesscited by 804
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
- 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
- 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
- 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
- 2018How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairnesscited by 48
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- 2020Algorithmic Fairness from a Non-ideal Perspectivecited by 30
- 2022The Forgotten Margins of AI Ethicscited by 30
12 more
- 2021Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systemscited by 27
- 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 25
- 2020Affirmative Algorithms: The Legal Grounds for Fairness as Awarenesscited by 15
- 2022Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophycited by 15
- 2020Dimensions of Diversity in Human Perceptions of Algorithmic Fairnesscited by 13
- 2021AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AIcited by 6
- 2024The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Actioncited by 6
- 2017Demographics and discussion influence views on algorithmic fairnesscited by 4
- 2020Exploring User Opinions of Fairness in Recommender Systemscited by 3
- 2024Structural Interventions and the Dynamics of Inequalitycited by 3
- 2024Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variabilitycited by 3
- 2021Who Gets What, According to Whom? An Analysis of Fairness Perceptions in Service Allocationcited by 2
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