2020/11/03 by Yunhe Feng, Feng, Yunhe, Daniel Saelid +7
Social Sciences · #Computational and Text Analysis Methods #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Privacy, Security, and Data Protection
paper · pdf · doi:10.48550/arxiv.2011.02066
openalex publication_date 2020/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) group at University of Washington participated in 2020 TREC Fairness Ranking Track. This report describes that track, assigned data and tasks, our group definitions, and our results. Our approach to bringing fairness in retrieval and re-ranking tasks with Semantic Scholar data was to extract various dimensions of author identity. These dimensions included gender and location. We developed modules for these extractions in a way that allowed us to plug them in for either of the tasks as needed. After trying different combinations of relative weights assigned to relevance, gender, and location information, we chose five runs for retrieval and five runs for re-ranking tasks. The results showed that our runs performed below par for re-ranking task, but above average for retrieval.