Overview of the TREC 2022 Fair Ranking Track

TL;DR

The paper reviews the 2022 TREC Fair Ranking Track, which focused on developing algorithms that ensure fair exposure for documents representing protected demographics in Wikipedia editing tasks.

Contribution

It introduces a resource allocation task aimed at promoting fair exposure of protected characteristics in Wikipedia editing, addressing systemic biases.

Findings

Implemented a fair ranking evaluation framework.

Demonstrated potential for reducing bias in document exposure.

Highlighted importance of fairness in information retrieval.

Abstract

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.