Overview of the TREC 2020 Fair Ranking Track
This work addresses fairness in search rankings for academic authors, but it is incremental as it builds on previous TREC tracks.
The paper describes the TREC 2020 Fair Ranking track, which tackled the problem of providing fair exposure to different groups of authors in academic search by setting up tasks for reranking and retrieval, with results evaluated based on fairness and relevance metrics.
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.