# Overview of the TREC 2022 Fair Ranking Track

arXiv: https://arxiv.org/abs/2302.05558 · HTML: https://mlsanity.com/paper/2302.05558/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2023.

## Built on by (most cited first, 7 of 7)

- [Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2409.19804/) (2024)
- [Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks](https://mlsanity.com/paper/2509.22486/) (2025)
- [Evaluating the Effect of Retrieval Augmentation on Social Biases](https://mlsanity.com/paper/2502.17611/) (2025)
- [Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers](https://mlsanity.com/paper/2603.10332/) (2026)
- [CoverageBench: Evaluating Information Coverage across Tasks and Domains](https://mlsanity.com/paper/2603.20034/) (2026)
- [Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias](https://mlsanity.com/paper/2603.24218/) (2026)
- [Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement](https://mlsanity.com/paper/2506.22372/) (2025)

## Abstract (arXiv)

The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents topical content or authors. The 2022 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.