Work

Translating intersectionality to fair machine learning in health sciences

Year: 2023

Type: article

Abstract: Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

Source: Nature Machine Intelligence

DOI: 10.1038/s42256-023-00651-3

Authors: Elle Lett, William G. La Cava

Institutions: Boston Children's Hospital, University of Pennsylvania, Harvard University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold

Estimated APC paid: $11,690