Corpus ID: 214667072

Overview of the TREC 2019 Fair Ranking Track

@article{Biega2020OverviewOT,
  title={Overview of the TREC 2019 Fair Ranking Track},
  author={Asia J. Biega and Fernando D. Diaz and Michael D. Ekstrand and Sebastian Kohlmeier},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.11650}
}
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… Expand

References

SHOWING 1-10 OF 10 REFERENCES
Ranking with Fairness Constraints
FA*IR: A Fair Top-k Ranking Algorithm
Fairness of Exposure in Rankings
Relevance and ranking in online dating systems
Measuring Fairness in Ranked Outputs
Equity of Attention: Amortizing Individual Fairness in Rankings
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
Multisided Fairness for Recommendation