# Selection Problems in the Presence of Implicit Bias

arXiv: https://arxiv.org/abs/1801.03533 · HTML: https://mlsanity.com/paper/1801.03533/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 29 of 29)

- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Fairness in Ranking under Uncertainty](https://mlsanity.com/paper/2107.06720/) (2021)
- [Fair Ranking with Noisy Protected Attributes](https://mlsanity.com/paper/2211.17067/) (2022)
- [Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers](https://mlsanity.com/paper/2404.03192/) (2024)
- [Fair Sequential Selection Using Supervised Learning Models](https://mlsanity.com/paper/2110.13986/) (2021)
- [Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making](https://mlsanity.com/paper/2205.04790/) (2022)
- [On Fair Selection in the Presence of Implicit and Differential Variance](https://mlsanity.com/paper/2112.05630/) (2021)
- [Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints](https://mlsanity.com/paper/2202.01661/) (2022)
- [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://mlsanity.com/paper/2305.02806/) (2023)
- [Fairness in Ranking under Disparate Uncertainty](https://mlsanity.com/paper/2309.01610/) (2023)
- [On Meritocracy in Optimal Set Selection](https://mlsanity.com/paper/2102.11932/) (2021)
- [The Equity Framework: Fairness Beyond Equalized Predictive Outcomes](https://mlsanity.com/paper/2205.01072/) (2022)
- [Who Pays? Personalization, Bossiness and the Cost of Fairness](https://mlsanity.com/paper/2209.04043/) (2022)
- [Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions](https://mlsanity.com/paper/2306.09835/) (2023)
- [Sampling Individually-Fair Rankings that are Always Group Fair](https://mlsanity.com/paper/2306.11964/) (2023)
- [Trading-off price for data quality to achieve fair online allocation](https://mlsanity.com/paper/2306.13440/) (2023)
- [A Mathematical Framework for AI-Human Integration in Work](https://mlsanity.com/paper/2505.23432/) (2025)
- [On Comparing Fair Classifiers under Data Bias](https://mlsanity.com/paper/2302.05906/) (2023)
- [Correcting Underrepresentation and Intersectional Bias for Classification](https://mlsanity.com/paper/2306.11112/) (2023)
- [Learning Fair Policies for Multi-stage Selection Problems from Observational Data](https://mlsanity.com/paper/2312.13173/) (2023)
- [Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes](https://mlsanity.com/paper/2505.14388/) (2025)
- [Affirmative Action vs. Affirmative Information](https://mlsanity.com/paper/2102.10019/) (2021)
- [Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented?](https://mlsanity.com/paper/2209.08665/) (2022)
- [The Initial Screening Order Problem](https://mlsanity.com/paper/2307.15398/) (2023)
- [Ranking with Slot Constraints](https://mlsanity.com/paper/2310.17870/) (2023)
- [Fair Active Ranking from Pairwise Preferences](https://mlsanity.com/paper/2402.03252/) (2024)
- [Centralized Selection with Preferences in the Presence of Biases](https://mlsanity.com/paper/2409.04897/) (2024)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [Generalizing Fair Top-k Selection: An Integrative Approach](https://mlsanity.com/paper/2603.04689/) (2026)

## Abstract (arXiv)

Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconscious biases about membership in particular groups can have an effect on their decision-making, even when they have no deliberate intention to discriminate against members of these groups. A growing body of experimental work has pointed to the effect that implicit bias can have in producing adverse outcomes. Here we propose a theoretical model for studying the effects of implicit bias on selection decisions, and a way of analyzing possible procedural remedies for implicit bias within this model. A canonical situation represented by our model is a hiring setting: a recruiting committee is trying to choose a set of finalists to interview among the applicants for a job, evaluating these applicants based on their future potential, but their estimates of potential are skewed by implicit bias against members of one group. In this model, we show that measures such as the Rooney Rule, a requirement that at least one of the finalists be chosen from the affected group, can not only improve the representation of this affected group, but also lead to higher payoffs in absolute terms for the organization performing the recruiting. However, identifying the conditions under which such measures can lead to improved payoffs involves subtle trade-offs between the extent of the bias and the underlying distribution of applicant characteristics, leading to novel theoretical questions about order statistics in the presence of probabilistic side information.