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Bias in AI: face, gender, and class disparities, mitigation strategies, and legal & ethical implications

Year: 2025

Type: article

Abstract: This paper explains the origins, implications and reasons for biases in Artificial Intelligence (AI) algorithms with an emphasis on methods for mitigation and their effects. Such biases explored include race, gender, class, and other relevant marginalized characteristics. Potential mitigation implications include ethical, legal and privacy concerns for companies and end-users alike.

Source: SUNY Digital Repository Support (State University of New York System)

Author: Burnside, Kyra

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W7223425966

FWCI:

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