Overview of the TREC 2021 Fair Ranking Track
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2023.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 1.
7 papers.
- 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankerscited by 17
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
- 2024Measuring Bias in a Ranked List using Term-based Representationscited by 4
- 2023The Role of Relevance in Fair Rankingcited by 3
- 2025What's in a Query: Polarity-Aware Distribution-Based Fair Rankingcited by 1
- 2024Enriching Social Science Research via Survey Item Linkingcited by 0
- 2026Plans for Evaluating Structured Generative Search Summariescited by 0
- 2026Plans for Evaluating Structured Generative Search Summaries26 May 2026
- 2025What's in a Query: Polarity-Aware Distribution-Based Fair Ranking17 Feb 2025
- 2024Enriching Social Science Research via Survey Item Linking20 Dec 2024
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Access19 May 2024
- 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers4 Apr 2024
- 2024Measuring Bias in a Ranked List using Term-based Representations9 Mar 2024
- 2023The Role of Relevance in Fair Ranking9 May 2023