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Overview of the TREC 2020 Fair Ranking Track
Preprint   Open access

Overview of the TREC 2020 Fair Ranking Track

Asia J Biega, Fernando Diaz, Michael D Ekstrand, Sergey Feldman and Sebastian Kohlmeier
arXiv.org
11 Aug 2021
DOI: https://doi.org/10.48550/arxiv.2108.05135
url
https://doi.org/10.48550/arxiv.2108.05135View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Computers and Society Computer Science - Information Retrieval Computer Science - Learning
This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For 2020, we again adopted an academic search task, where we have a corpus of academic article abstracts and queries submitted to a production academic search engine. The central goal of the Fair Ranking track is to provide fair exposure to different groups of authors (a group fairness framing). We recognize that there may be multiple group definitions (e.g. based on demographics, stature, topic) and hoped for the systems to be robust to these. We expected participants to develop systems that optimize for fairness and relevance for arbitrary group definitions, and did not reveal the exact group definitions until after the evaluation runs were submitted.The track contains two tasks,reranking and retrieval, with a shared evaluation.

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