# Fairness in Information Access Systems

arXiv: https://arxiv.org/abs/2105.05779 · HTML: https://mlsanity.com/paper/2105.05779/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2021.

## Built on by (most cited first, 29 of 29)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.11598/) (2024)
- [Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach](https://mlsanity.com/paper/2109.08549/) (2021)
- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies](https://mlsanity.com/paper/2502.00690/) (2025)
- [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://mlsanity.com/paper/2305.02806/) (2023)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [Measuring Bias in a Ranked List using Term-based Representations](https://mlsanity.com/paper/2403.05975/) (2024)
- [A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System](https://mlsanity.com/paper/2405.02219/) (2024)
- [The Role of Relevance in Fair Ranking](https://mlsanity.com/paper/2305.05608/) (2023)
- [A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems](https://mlsanity.com/paper/2402.00485/) (2024)
- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations](https://mlsanity.com/paper/2609.04047/) (2026)
- [Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF](https://mlsanity.com/paper/2309.08621/) (2023)
- [Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems](https://mlsanity.com/paper/2506.21617/) (2025)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness](https://mlsanity.com/paper/2508.05680/) (2025)
- [An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching](https://mlsanity.com/paper/2207.14367/) (2022)
- [Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems](https://mlsanity.com/paper/2209.03819/) (2022)
- [Systemic Fairness](https://mlsanity.com/paper/2304.06901/) (2023)
- [Learning to Re-rank with Constrained Meta-Optimal Transport](https://mlsanity.com/paper/2305.00319/) (2023)
- [The Initial Screening Order Problem](https://mlsanity.com/paper/2307.15398/) (2023)
- [Social Choice for Heterogeneous Fairness in Recommendation](https://mlsanity.com/paper/2410.04551/) (2024)
- [Statistically Testing Training Data for Unwanted Error Patterns using Rule-Oriented Regression](https://mlsanity.com/paper/2503.18497/) (2025)
- [Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto](https://mlsanity.com/paper/2506.11112/) (2025)
- [Bias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination](https://mlsanity.com/paper/2610.14015/) (2026)
- [Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems](https://mlsanity.com/paper/2610.14579/) (2026)

## Abstract (arXiv)

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.