Year: 2019
Type: preprint
Abstract: While search efficacy has been evaluated traditionally on the basis of result relevance, fairness of search has attracted recent attention. In this work, we define a notion of distributional fairness and provide a conceptual framework for evaluating search results based on it. As part of this, we formulate a set of axioms which an ideal evaluation framework should satisfy for distributional fairness. We show how existing TREC test collections can be repurposed to study fairness, and we measure potential data bias to inform test collection design for fair search. A set of analyses show metric divergence between relevance and fairness, and we describe a simple but flexible interpolation strategy for integrating relevance and fairness into a single metric for optimization and evaluation.
Source: arXiv (Cornell University)
Authors: Das, Anubrata, Lease, Matthew
Institution: The University of Texas at Austin
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W2962816295