  The goal of the TREC Fair Ranking track was to develop a benchmark for
evaluating retrieval systems in terms of fairness to different content
providers in addition to classic notions of relevance. As part of the
benchmark, we defined standardized fairness metrics with evaluation protocols
and released a dataset for the fair ranking problem. The 2019 task focused on
reranking academic paper abstracts given a query. The objective was to fairly
represent relevant authors from several groups that were unknown at the system
submission time. Thus, the track emphasized the development of systems which
have robust performance across a variety of group definitions. Participants
were provided with querylog data (queries, documents, and relevance) from
Semantic Scholar. This paper presents an overview of the track, including the
task definition, descriptions of the data and the annotation process, as well
as a comparison of the performance of submitted systems.
