# Exploring User Opinions of Fairness in Recommender Systems

arXiv: https://arxiv.org/abs/2003.06461 · HTML: https://mlsanity.com/paper/2003.06461/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [Fairness and Transparency in Recommendation: The Users' Perspective](https://mlsanity.com/paper/2103.08786/) (2021)
- [Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms](https://mlsanity.com/paper/2505.07339/) (2025)

## Abstract (arXiv)

Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing accuracy for users and fairness to providers. But what is fair in the context of recommendation--particularly when there are multiple stakeholders? In an initial exploration of this problem, we ask users what their ideas of fair treatment in recommendation might be, and why. We analyze what might cause discrepancies or changes between user's opinions towards fairness to eventually help inform the design of fairer and more transparent recommendation algorithms.