# Fairness and Transparency in Recommendation: The Users' Perspective

arXiv: https://arxiv.org/abs/2103.08786 · HTML: https://mlsanity.com/paper/2103.08786/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [Opportunistic Multi-aspect Fairness through Personalized Re-ranking](https://mlsanity.com/paper/2005.12974/) (2020)
- [Exploring User Opinions of Fairness in Recommender Systems](https://mlsanity.com/paper/2003.06461/) (2020)

## Built on by (most cited first, 10 of 10)

- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation](https://mlsanity.com/paper/2202.06466/) (2022)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Towards Selection as Power: Bounding Decision Authority in Autonomous Agents](https://mlsanity.com/paper/2602.14606/) (2026)
- [Elucidate Gender Fairness in Singing Voice Transcription](https://mlsanity.com/paper/2308.02898/) (2023)
- [What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders](https://mlsanity.com/paper/2401.11632/) (2024)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)

## Abstract (arXiv)

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.