Mitigation measures for addressing gender bias in artificial intelligence within healthcare settings: a critical area of sociological inquiry

Anna Isaksson

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Sex and Gender in Healthcare

Abstract

Abstract Artificial intelligence (AI) is often described as crucial for making healthcare safer and more efficient. However, some studies point in the opposite direction, demonstrating how biases in AI cause inequalities and discrimination. As a result, a growing body of research suggests mitigation measures to avoid gender bias. Typically, mitigation measures address various stakeholders such as the industry, academia, and policy-makers. To the author’s knowledge, these have not undergone sociological analysis. The article fills this gap and explores five examples of mitigation measures designed to counteract gender bias in AI within the healthcare sector. The rapid development of AI in healthcare plays a crucial role globally and must refrain from creating or reinforcing inequality and discrimination. In this effort, mitigation measures to avoid gender bias in AI in healthcare are central tools and, therefore, essential to explore from a social science perspective, including sociology. Sociologists have made valuable contributions to studying inequalities and disparities in AI. However, research has pointed out that more engagement is needed, specifically regarding bias in AI. While acknowledging the importance of these measures, the article suggests that they lack accountable agents for implementation and overlook potential implementation barriers such as resistance, power relations, and knowledge hierarchies. Recognizing the conditions where the mitigation measures are to be implemented is essential for understanding the potential challenges that may arise. Consequently, more studies are needed to explore the practical implementation of mitigation measures from a social science perspective and a systematic review of mitigation measures.

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References

2009Qualitative Research: A Guide to Design and Implementation 2018Ensuring Fairness in Machine Learning to Advance Health Equity 2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias 2019Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations 2022A sociotechnical perspective for the future of AI: narratives, inequalities, and human control 2021Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change 2022Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates 2023Gender bias perpetuation and mitigation in AI technologies: challenges and opportunities

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