Teaching Justice: Mitigating Bias in Machine Learning

Edited by Dr. Carlotta A. Berry and Dr. Brandeis Hill Marshall, two leading Black American women in STEM, the book addresses a longstanding gap in the field by offering a comprehensive and socially responsive guide to understanding and mitigating bias in artificial intelligence systems. Most of all, these two technologists offer a clear, technologically sound set of instructions for responsible model design and maintenance, accompanied by helpful charts and figures.

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Publication Details

Journal
Stanford OJS Journals
Published
2025-04-03
DOI
https://doi.org/10.60690/8dxytg88
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Teaching Justice: Mitigating Bias in Machine Learning

Stanford OJS Journals
Ethics and Social Impacts of AI
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Teaching Justice: Mitigating Bias in Machine Learning

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Abstract

Edited by Dr. Carlotta A. Berry and Dr. Brandeis Hill Marshall, two leading Black American women in STEM, the book addresses a longstanding gap in the field by offering a comprehensive and socially responsive guide to understanding and mitigating bias in artificial intelligence systems. Most of all, these two technologists offer a clear, technologically sound set of instructions for responsible model design and maintenance, accompanied by helpful charts and figures.

Stanford OJS Journals
Stanford University (US)
Gender equality
Openalex Percentile: Top 38%
Ethics and Social Impacts of AI
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