Authorship attribution models misattribute texts to some authors far more often than chance, and the risk is highest for authors whose author embeddings sit near the centroid.
Overview of the TREC 2019 Fair Ranking Track
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Quantifying Misattribution Unfairness in Authorship Attribution
Authorship attribution models misattribute texts to some authors far more often than chance, and the risk is highest for authors whose author embeddings sit near the centroid.