Work

Pairwise Fairness in Ranking as a Dissatisfaction Measure

Year: 2023

Type: conference-paper

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.

Source: ACM International Conference on Web Search and Data Mining (WSDM)

DOI: 10.1145/3539597.3570459

Authors: Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, Asia J. Biega

Institutions: Max Planck Institute for Security and Privacy, University of Padua

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funder: Horizon 2020 Framework Programme