George Zerveas, Navid Rekabsaz, D. Cohen, Carsten Eickhoff,
"Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization"
: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), 7-2022
Original Titel:
Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022)
Original Kurzfassung:
Societal biases can influence Information Retrieval system results,
and conversely, search results can potentially reinforce existing societal
biases. Recent research has therefore focused on developing
methods for quantifying and mitigating bias in search results
and applied them to contemporary retrieval systems that leverage
transformer-based language models. In the present work, we expand
this direction of research by considering bias mitigation within a
framework for contextual document embedding reranking. In this
framework, the transformer-based query encoder is optimized for
relevance ranking through a list-wise objective, by jointly scoring
for the same query a large set of candidate document embeddings
in the context of one another, instead of in isolation. At the same
time, we impose a regularization loss which penalizes highly scoring
documents that deviate from neutrality with respect to a protected attribute
(e.g., gender). Our approach for bias mitigation is end-to-end
differentiable and efficient. Compared to the existing alternatives
for deep neural retrieval architectures, which are based on adversarial
training, we demonstrate that it can attain much stronger bias
mitigation/fairness. At the same time, for the same amount of bias
mitigation, it offers significantly better relevance performance (utility).
Crucially, our method allows for a more finely controllable
and predictable intensity of bias mitigation, which is essential for
practical deployment in production systems.
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