# Bias Disparity in Recommendation Systems

arXiv: https://arxiv.org/abs/1811.01461 · HTML: https://mlsanity.com/paper/1811.01461/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness Through Regularization for Learning to Rank](https://mlsanity.com/paper/2102.05996/) (2021)
- [Dissecting Bias of ChatGPT in College Major Recommendations](https://mlsanity.com/paper/2401.11699/) (2023)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation](https://mlsanity.com/paper/2109.05931/) (2021)
- [FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software](https://mlsanity.com/paper/2305.14396/) (2023)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [Fair Augmentation for Graph Collaborative Filtering](https://mlsanity.com/paper/2408.12208/) (2024)
- [Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace](https://mlsanity.com/paper/2203.03718/) (2022)
- [Text-to-Image Representativity Fairness Evaluation Framework](https://mlsanity.com/paper/2410.14201/) (2024)
- [Fuck the Algorithm: Conceptual Issues in Algorithmic Bias](https://mlsanity.com/paper/2505.13509/) (2025)
- [WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation](https://mlsanity.com/paper/2602.17442/) (2026)

## Abstract (arXiv)

Recommender systems have been applied successfully in a number of different domains, such as, entertainment, commerce, and employment. Their success lies in their ability to exploit the collective behavior of users in order to deliver highly targeted, personalized recommendations. Given that recommenders learn from user preferences, they incorporate different biases that users exhibit in the input data. More importantly, there are cases where recommenders may amplify such biases, leading to the phenomenon of bias disparity. In this short paper, we present a preliminary experimental study on synthetic data, where we investigate different conditions under which a recommender exhibits bias disparity, and the long-term effect of recommendations on data bias. We also consider a simple re-ranking algorithm for reducing bias disparity, and present some observations for data disparity on real data.