On Explaining Unfairness: An Overview

• URL: http://arxiv.org/abs/2402.10762v1
• Date: Fri, 16 Feb 2024 15:38:00 GMT
• Title: On Explaining Unfairness: An Overview
• Authors: Christos Fragkathoulas, Vasiliki Papanikou, Danae Pla Karidi, Evaggelia Pitoura
• Abstract summary: Algorithmic fairness and explainability are foundational elements for achieving responsible AI. We categorize fairness into three types: (a) Explanations to enhance fairness metrics, (b) Explanations to help us understand the causes of (un)fairness, and (c) Explanations to assist us in designing methods for mitigating unfairness.
• Score: 2.0277446818411
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, a research area that is recently receiving increasing attention. To this end, we first present two comprehensive taxonomies, each representing one of the two complementary fields of study: fairness and explanations. Then, we categorize explanations for fairness into three types: (a) Explanations to enhance fairness metrics, (b) Explanations to help us understand the causes of (un)fairness, and (c) Explanations to assist us in designing methods for mitigating unfairness. Finally, based on our fairness and explanation taxonomies, we present undiscovered literature paths revealing gaps that can serve as valuable insights for future research.

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