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

## TL;DR

This paper systematically reviews bias mitigation techniques in recommender systems considering AI feedback loops, providing a taxonomy and identifying gaps such as lack of shared simulators and inconsistent evaluation metrics.

## Contribution

It offers a comprehensive taxonomy of bias mitigation methods tested in multi-round settings and highlights key research gaps and practical challenges in the field.

## Key findings

- Few studies use shared simulators.
- Most evaluate either fairness or performance, not both.
- Limited research on long-term bias mitigation in feedback loops.

## Abstract

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.