Articlegreen2022

Mitigating Racial Bias in Machine Learning

Kristin M. Kostick · Ivan Glenn Cohen · Sara Gerke · Bernard Lo · James Antaki · Faezah Movahedi +4 more

77 citations0/0 files storedThe Journal of Law Medicine & Ethics

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI)

Abstract

When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.

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References

2019Dissecting racial bias in an algorithm used to manage the health of populations 2017Semantics derived automatically from language corpora contain human-like biases 2017Standards for UNiversal reporting of patient Decision Aid Evaluation studies: the development of SUNDAE Checklist 2015Bias, Black Lives, and Academic Medicine

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2024The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review 2023Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic 2022An AI Bill of Rights: Implications for Health Care AI and Machine Learning—A Bioethics Lens 2023Assessing and Mitigating Bias in Artificial Intelligence: A Review 2023Out of their minds? Externalist challenges for using AI in forensic psychiatry 2025Distinct predictors of positive attitudes toward artificial intelligence and general technology: big five traits, gender, and age 2025Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review 2025Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis

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