# On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback

arXiv: https://arxiv.org/abs/2503.05662 · HTML: https://mlsanity.com/paper/2503.05662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Explore First, Exploit Next: The True Shape of Regret in Bandit Problems](https://mlsanity.com/paper/1602.07182/) (2016)
- [Online Learning with an Unknown Fairness Metric](https://mlsanity.com/paper/1802.06936/) (2018)
- [Rotting Bandits](https://mlsanity.com/paper/1702.07274/) (2017)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [Fairness of Exposure in Stochastic Bandits](https://mlsanity.com/paper/2103.02735/) (2021)
- [Efficient Automatic CASH via Rising Bandits](https://mlsanity.com/paper/2012.04371/) (2020)
- [Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates](https://mlsanity.com/paper/1711.03591/) (2017)
- [Cleaning up the neighborhood: A full classification for adversarial partial monitoring](https://mlsanity.com/paper/1805.09247/) (2018)
- [Fair Sequential Selection Using Supervised Learning Models](https://mlsanity.com/paper/2110.13986/) (2021)
- [Group Fairness in Bandit Arm Selection](https://mlsanity.com/paper/1912.03802/) (2019)
- [Mathematical model of gender bias and homophily in professional hierarchies](https://mlsanity.com/paper/1901.07600/) (2019)
- [Non-stochastic Bandits With Evolving Observations](https://mlsanity.com/paper/2405.16843/) (2024)
- [The price of unfairness in linear bandits with biased feedback](https://mlsanity.com/paper/2203.09784/) (2022)
- [Complete Policy Regret Bounds for Tallying Bandits](https://mlsanity.com/paper/2204.11174/) (2022)
- [Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality](https://mlsanity.com/paper/2305.02955/) (2023)
- [Identification of homophily and preferential recruitment in respondent-driven sampling](https://mlsanity.com/paper/1511.05397/) (2015)

## Abstract (arXiv)

Unconscious bias has been shown to influence how we assess our peers, with consequences for hiring, promotions and admissions. In this work, we focus on affinity bias, the component of unconscious bias which leads us to prefer people who are similar to us, despite no deliberate intention of favoritism. In a world where the people hired today become part of the hiring committee of tomorrow, we are particularly interested in understanding (and mitigating) how affinity bias affects this feedback loop. This problem has two distinctive features: 1) we only observe the biased value of a candidate, but we want to optimize with respect to their real value 2) the bias towards a candidate with a specific set of traits depends on the fraction of people in the hiring committee with the same set of traits. We introduce a new bandits variant that exhibits those two features, which we call affinity bandits. Unsurprisingly, classical algorithms such as UCB often fail to identify the best arm in this setting. We prove a new instance-dependent regret lower bound, which is larger than that in the standard bandit setting by a multiplicative function of K. Since we treat rewards that are time-varying and dependent on the policy's past actions, deriving this lower bound requires developing proof techniques beyond the standard bandit techniques. Finally, we design an elimination-style algorithm which nearly matches this regret, despite never observing the real rewards.