Work

A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges

Year: 2023

Type: conference-paper

Abstract: The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.

Source: International Joint Conference on Artificial Intelligence

DOI: 10.24963/ijcai.2023/742

Authors: Usman Gohar, Lu Cheng

Institutions: Iowa State University, University of Illinois Chicago

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Gender equality, Peace, justice, and strong institutions

Open Access status: bronze

Funder: Cisco Systems