# Overview of the TREC 2021 Fair Ranking Track

arXiv: https://arxiv.org/abs/2302.10856 · HTML: https://mlsanity.com/paper/2302.10856/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2023.

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

- [Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers](https://mlsanity.com/paper/2404.03192/) (2024)
- [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024)
- [Measuring Bias in a Ranked List using Term-based Representations](https://mlsanity.com/paper/2403.05975/) (2024)
- [The Role of Relevance in Fair Ranking](https://mlsanity.com/paper/2305.05608/) (2023)
- [What's in a Query: Polarity-Aware Distribution-Based Fair Ranking](https://mlsanity.com/paper/2502.11429/) (2025)
- [Enriching Social Science Research via Survey Item Linking](https://mlsanity.com/paper/2412.15831/) (2024)
- [Plans for Evaluating Structured Generative Search Summaries](https://mlsanity.com/paper/2605.26400/) (2026)

## 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 2021 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 2021 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.