Reviewgold2022

Conceptualising fairness: three pillars for medical algorithms and health equity

Laura Sikstrom · Marta M. Maslej · Katrina Hui · Zoe Findlay · Daniel Z. Buchman · Sean Hill

76 citations0/0 files storedBMJ Health & Care Informatics

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

Abstract

OBJECTIVES: Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature. METHODS: We conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo). RESULTS: Our synthesis identified 'Three Pillars for Fairness': transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare. DISCUSSION: We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients. CONCLUSION: We conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.

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References

1991Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color 2019Dissecting racial bias in an algorithm used to manage the health of populations 1958Justice as Fairness 2012CRITICAL QUESTIONS FOR BIG DATA 2018AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2012Fairness through awareness 2016How the machine ‘thinks’: Understanding opacity in machine learning algorithms

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