Pairwise Fairness in Ranking as a Dissatisfaction Measure

Alessandro Fabris · Gianmaria Silvello · Gian Antonio Susto · Asia J. Biega

26 citations0/0 files storedACM International Conference on Web Search and Data Mining (WSDM)

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Ethics and Social Impacts of AI Information Retrieval and Search Behavior Recommender Systems and Techniques

Abstract

Fairness and equity have become central to ranking problems in information access systems, such as search engines, recommender systems, or marketplaces. To date, several types of fair ranking measures have been proposed, including diversity, exposure, and pairwise fairness measures. Out of those, pairwise fairness is a family of metrics whose normative grounding has not been clearly explicated, leading to uncertainty with respect to the construct that is being measured and how it relates to stakeholders' desiderata.

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1938A NEW MEASURE OF RANK CORRELATION 2018Fairness of Exposure in Rankings 2017FA*IR 2017Measuring Fairness in Ranked Outputs 2019Fairness in Recommendation Ranking through Pairwise Comparisons 2021Measurement and Fairness 2018Equity of Attention 2019Designing Fair Ranking Schemes

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