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The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query.
Evaluating stochastic rankings with expected exposure
F. Diaz, B. Mitra, M. D. Ekstrand, A. J. Biega, and B. Carterette · 2004
Earlier work this paper cites.
Discrimination-aware data mining
D. Pedreshi, S. Ruggieri, and F. Turini · 2008
Earlier work this paper cites.
Comparing fair ranking metrics
A. Raj, C. Wood, A. Montoly, and M. D. Ekstrand · 2009
Earlier work this paper cites.
Quantifying the impact of user attentionon fair group representation in ranked lists
P. Sapiezynski, W. Zeng, R. E Robertson, A. Mislove, and C. Wilson · 2019
Cited alongside, same era.
Keybert: Minimal keyword extraction with bert., 2020
M. Grootendorst · 2020
Cited alongside, same era.
Algorithmic fairness: Choices, assumptions, and definitions
S. Mitchell, E. Potash, S. Barocas, A. D’Amour, and K. Lum · 2020
Cited alongside, same era.
A taxonomy of knowledge gaps for wikimedia projects (second draft)
M. Redi, M. Gerlach, I. Johnson, J. Morgan, and L. Zia · 2020
Later among the works it cites.
Ms. categorized: Gender, notability, and inequality on wikipedia
F. Tripodi · 2021
Later among the works it cites.
C. Pinney, A. Raj, A. Hanna, and M. D. Ekstrand · 2023
Closest in time.
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