# Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy

arXiv: https://arxiv.org/abs/2509.00109 · HTML: https://mlsanity.com/paper/2509.00109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [Deep Bayesian Bandits: Exploring in Online Personalized Recommendations](https://mlsanity.com/paper/2008.00727/) (2020)
- [Bandit Learning with Delayed Impact of Actions](https://mlsanity.com/paper/2002.10316/) (2020)
- [Exploring Longitudinal Effects of Session-based Recommendations](https://mlsanity.com/paper/2008.07226/) (2020)
- [Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms](https://mlsanity.com/paper/2309.07107/) (2023)
- [Modeling and Counteracting Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2001.04832/) (2020)
- [Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval](https://mlsanity.com/paper/2008.02930/) (2020)
- [Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop](https://mlsanity.com/paper/2405.17998/) (2024)
- [Enhancing New-item Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2504.21362/) (2025)
- [Unbiased Learning to Rank with Biased Continuous Feedback](https://mlsanity.com/paper/2303.04335/) (2023)

## Built on by (most cited first, 1 of 1)

- [BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests](https://mlsanity.com/paper/2609.08725/) (2026)

## Abstract (arXiv)

Recommender systems continually retrain on user reactions to their own predictions, creating AI feedback loops that amplify biases and diminish fairness over time. Despite this well-known risk, most bias mitigation techniques are tested only on static splits, so their long-term fairness across multiple retraining rounds remains unclear. We therefore present a systematic literature review of bias mitigation methods that explicitly consider AI feedback loops and are validated in multi-round simulations or live A/B tests. Screening 347 papers yields 24 primary studies published between 2019-2025. Each study is coded on six dimensions: mitigation technique, biases addressed, dynamic testing set-up, evaluation focus, application domain, and ML task, organising them into a reusable taxonomy. The taxonomy offers industry practitioners a quick checklist for selecting robust methods and gives researchers a clear roadmap to the field's most urgent gaps. Examples include the shortage of shared simulators, varying evaluation metrics, and the fact that most studies report either fairness or performance; only six use both.