Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints
Not on the map. Why?
Abstract (arXiv)
In selection processes such as hiring, promotion, and college admissions, implicit bias toward socially-salient attributes such as race, gender, or sexual orientation of candidates is known to produce persistent inequality and reduce aggregate utility for the decision maker. Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection. Recent works have established that such lower-bound constraints can be very effective in improving aggregate utility in the case when each individual belongs to at most one affected group. However, in several settings, individuals may belong to multiple affected groups and, consequently, face more extreme implicit bias due to this intersectionality. We consider independently drawn utilities and show that, in the intersectional case, the aforementioned non-intersectional constraints can only recover part of the total utility achievable in the absence of implicit bias. On the other hand, we show that if one includes appropriate lower-bound constraints on the intersections, almost all the utility achievable in the absence of implicit bias can be recovered. Thus, intersectional constraints can offer a significant advantage over a reductionist dimension-by-dimension non-intersectional approach to reducing inequality.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 94 references that are in the map. First posted on arXiv 3 Feb 2022.
Builds on
5 of 94 references.
References · 5
- 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 134
- 2019Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?cited by 50
- 2018Selection Problems in the Presence of Implicit Biascited by 29
- 2020Interventions for Ranking in the Presence of Implicit Biascited by 27
- 2021On Fair Selection in the Presence of Implicit and Differential Variancecited by 9
Built on by
Citing papers per year: 2022: 1; 2023: 3; 2024: 1.
5 papers.
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biasescited by 5
- 2023Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functionscited by 2
- 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
- 2022Turtle Score -- Similarity Based Developer Analyzercited by 0
- 2024Centralized Selection with Preferences in the Presence of Biasescited by 0
- 2024Centralized Selection with Preferences in the Presence of Biases7 Sep 2024
- 2023Sampling Individually-Fair Rankings that are Always Group Fair21 Jun 2023
- 2023Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions16 Jun 2023
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biases3 May 2023
- 2022Turtle Score -- Similarity Based Developer Analyzer10 May 2022