Intersectional Approach to Algorithmic Discrimination in Healthcare

Name: Intersectional Approach to Algorithmic Discrimination in Healthcare | A Comparative Legal Perspective
Brand: Nomos
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A Comparative Legal Perspective

Malwina Anna Wójcik-Suffia(Author)

Nomos (Publisher)

1st Edition

Published on 22. May 2026

329 pages

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978-3-7489-7121-4 (ISBN)

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Preface
Acknowledgements
I come to you
Chapter 1. Introduction
1.1. Intersectional discrimination in clinical algorithms - a new old challenge?
1.2. Objectives of the book and research questions
1.3. Methodology
1.3.1. Legal informatics approach
1.3.2. Comparative legal approach
1.4. Overview of the book
Chapter 2. Intersectionality - From a Theoretical Framework to Implementation in Health Equity Research and Computer Science
2.1. Introduction
2.2. Intersectionality as a tool to address health disparities
2.2.1. The individual strand of intersectionality
2.2.1.1. The indivisibility of experience
2.2.1.2. The dynamics of sameness and difference in group disadvantage
2.2.2. The contextual strand
2.2.3. Understanding the interplay between the individual and contextual strands - case studies
2.2.3.1. Domestic and sexual violence against ethnic and racial minority women
2.2.3.2. Involuntary anticonception and sterilisation of ethnic and racial minority women - the illusion of choice
2.3. Operationalising intersectionality in health disparities research
2.3.1. Anticategorical approach
2.3.2. Intracategorical approach
2.3.3. Intercategorical approach
2.4. Intersectionality in computer science
2.4.1. Intersectional bias in clinical algorithms
2.4.2. Synthetic data as a strategy to address the shortage of intersectional data
2.4.3. Intersectional fairness metrics
2.4.3.1. Fairness metrics manifestly incompatible with intersectionality
2.4.3.2. Towards intersectionality-sensitive fairness metrics
2.4.4. Power relations in intersectional fairness
2.5. Conclusions
Chapter 3. Algorithmic Intersectional Bias in Healthcare - The Response of Antidiscrimination Law
3.1. Introduction
3.2. Algorithmic bias and antidiscrimination law
3.2.1. US
3.2.1.1. Algorithmic discrimination and the theories of liability - between disparate treatment and disparate impact
3.2.1.2. Fairness interventions - the legality of algorithmic affirmative action
3.2.2. EU
3.2.2.1. Algorithmic discrimination and the theories of liability - between direct and indirect discrimination
3.2.2.2. Fairness interventions - the legality of algorithmic positive action
3.2.3. Comparative discussion
3.3. The development of intersectionality in antidiscrimination law
3.3.1. Failure to acknowledge patterns of difference and sameness in group disadvantage: The 'anti-canon' of intersectionality
3.3.2. Taming intersectionality - towards recognition of intersectional disadvantage
3.3.2.1. US
3.3.2.2. EU
3.3.3. Comparative discussion
3.4. The scope of legal protection against intersectional discrimination in healthcare
3.4.1. Protection against discrimination in healthcare in the US
3.4.1.1. Applicable law
3.4.1.2. The uncertain status of gender identity and sexual orientation as prohibited grounds of discrimination
3.4.1.3. Affordable Care Act's scope of protection against disparate impact in healthcare
3.4.1.4. Intersectional discrimination claims under the Affordable Care Act
3.4.1.5. Prohibition of discrimination in the use of patient care decision support tools in the new Section 1557 Rule
3.4.1.5.1. The obligations of healthcare providers to avoid discrimination
3.4.1.5.2. Algorithmic intersectional discrimination
3.4.1.5.3. The lack of extended data collection obligations
3.4.2. Protection against discrimination in healthcare in the EU
3.4.2.1. The applicable law
3.4.2.2. The fragmentation of protected grounds
3.4.2.2.1. Towards the judicial recognition of new discrimination grounds - the role of Art. 21 of the Charter of Fundamental Rights
3.4.2.2.2. Towards legislative action to broaden protected grounds and recognise intersectional discrimination - the potential impact of the proposed Horizontal Equality Directive
3.4.2.3. Remedying fragmented enforcement mechanisms - towards the reform of Equality Bodies
3.4.3. Comparative discussion
3.5. Conclusion
Chapter 4. Beyond Discrimination Law - Intersectional Bias Considerations in the Regulation of Clinical AI
4.1. Introduction
4.2. US
4.2.1. The FDA's regulation of medical devices
4.2.1.1. Definition and classification of medical devices
4.2.1.2. Bias considerations in pre-market conformity assessment
4.2.1.3. Bias considerations in post-market monitoring
4.2.2. HTI-1 Rule
4.2.2.1. Evidence-based DSI and Predictive DSI
4.2.2.2. Source attributes
4.2.2.3. Intervention Risk Management
4.2.3. The AI Bill of Rights
4.2.4. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
4.2.4.1. The regulation of bias in foundation models
4.3. EU
4.3.1. The Medical Device Regulation
4.3.1.1. The definition and classification of medical devices
4.3.1.2. Bias considerations in pre-marketing
4.3.1.3. Bias considerations in post-market monitoring
4.3.2. The Health Technology Assessment Regulation
4.3.3. The AI Act
4.3.3.1. Risk management system and algorithmic bias
4.3.3.2. Data fairness considerations in the AI Act
4.3.3.3. Fairness-related transparency measures
4.3.3.4. Fairness-related obligations of deployers
4.3.3.5. The rights of individuals affected by algorithmic bias
4.3.3.6. The role of the fundamental rights impact assessment
4.3.3.7. Addressing algorithmic discrimination on a systemic level
4.3.3.8. The regulation of bias in foundation models
4.3.4. The European Health Data Space
4.3.4.1. The impact of the European Health Data Space on the availability of data
4.3.4.2. The impact of the European Health Data Space on the quality of data
4.4. Comparative discussion
4.4.1. Between the sectorial and horizontal regulation of bias in clinical AI
4.4.2. The allocation of responsibility between clinical AI providers and deployers
4.4.3. Detection and mitigation of bias in foundation models
4.4.4. Intersectional considerations in the regulation of clinical AI
4.5. Conclusion
Chapter 5. Intersectionality Wheel for Clinical Algorithms - Toward a Framework for Assessing Intersectional Fairness
5.1. Introduction
5.2. Introducing the intersectionality wheel for clinical algorithms
5.2.1. Multidimensionality and entanglement of socio-biological categories
5.2.2. Focus on historically marginalised and oppressed groups
5.2.3. Power structures on the intersection of technology, medicine and law
5.3. Possible implementation of the intersectionality wheel
5.3.1. The contribution to intersectionality literature
5.3.2. Intersectionality wheel for clinical algorithms as a framework to develop a holistic intersectional fairness impact assessment
5.4. Conclusion
Chapter 6. Conclusions
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Intersectional Approach to Algorithmic Discrimination in Healthcare

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