Overview - Fair Ranking 2019

Proceedings | Data | Runs | Participants

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.

Track coordinator(s):

• Asia J. Biega, Microsoft Research Montréal
• Fernando Diaz, Microsoft Research Montréal
• Michael D. Ekstrand, Boise State University
• Sebastian Kohlmeier, Allen Institute for Artificial Intelligence

Track Web Page: https://fair-trec.github.io/

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