Intersectional Approach to Algorithmic Discrimination in Healthcare: A Comparative Legal Perspective

Malwina Anna Wójcik-Suffia

doi:10.5771/9783748971214

Zusammenfassung

The increasing use of AI in clinical decision-making offers powerful tools to address health challenges but also risks reinforcing inequalities. Clinical algorithms may reproduce intersectional discrimination arising from multiple protected grounds. While intersectionality is well established in social theory, it remains insufficient-ly operationalised in law and computer science. This book proposes an intersectional approach to developing and regulating AI-based clinical algorithms. Focusing on the EU and the US, it examines health disparities, algorithmic fairness, and antidiscrimination law, and proposes an intersectional fairness assessment frame-work for fair clinical AI.

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Bibliographische Angaben

Auflage: 1/2026
Copyrightjahr: 2026
ISBN-Print: 978-3-7560-4170-1
ISBN-Online: 978-3-7489-7121-4
Verlag: Nomos, Baden-Baden
Reihe: Luxemburger Juristische Studien - Luxembourg Legal Studies
Band: 27
Sprache: Englisch
Seiten: 331
Produkttyp: Monographie

Inhaltsverzeichnis

KapitelSeiten

1. Acknowledgements Kein Zugriff
2. I come to you Kein Zugriff
1. 1.1. Intersectional discrimination in clinical algorithms – a new old challenge? Kein Zugriff
2. 1.2. Objectives of the book and research questions Kein Zugriff
3. 1. 1.3.1. Legal informatics approach Kein Zugriff
  2. 1.3.2. Comparative legal approach Kein Zugriff
4. 1.4. Overview of the book Kein Zugriff
1. 2.1. Introduction Kein Zugriff
2. 1. 1. 2.2.1.1. The indivisibility of experience Kein Zugriff
    2. 2.2.1.2. The dynamics of sameness and difference in group disadvantage Kein Zugriff
  2. 2.2.2. The contextual strand Kein Zugriff
  3. 1. 2.2.3.1. Domestic and sexual violence against ethnic and racial minority women Kein Zugriff
    2. 2.2.3.2. Involuntary anticonception and sterilisation of ethnic and racial minority women – the illusion of choice Kein Zugriff
3. 1. 2.3.1. Anticategorical approach Kein Zugriff
  2. 2.3.2. Intracategorical approach Kein Zugriff
  3. 2.3.3. Intercategorical approach Kein Zugriff
4. 1. 2.4.1. Intersectional bias in clinical algorithms Kein Zugriff
  2. 2.4.2. Synthetic data as a strategy to address the shortage of intersectional data Kein Zugriff
  3. 1. 2.4.3.1. Fairness metrics manifestly incompatible with intersectionality Kein Zugriff
    2. 2.4.3.2. Towards intersectionality-sensitive fairness metrics Kein Zugriff
  4. 2.4.4. Power relations in intersectional fairness Kein Zugriff
5. 2.5. Conclusions Kein Zugriff
1. 3.1. Introduction Kein Zugriff
2. 1. 1. 3.2.1.1. Algorithmic discrimination and the theories of liability – between disparate treatment and disparate impact Kein Zugriff
    2. 3.2.1.2. Fairness interventions – the legality of algorithmic affirmative action Kein Zugriff
  2. 1. 3.2.2.1. Algorithmic discrimination and the theories of liability – between direct and indirect discrimination Kein Zugriff
    2. 3.2.2.2. Fairness interventions – the legality of algorithmic positive action Kein Zugriff
  3. 3.2.3. Comparative discussion Kein Zugriff
3. 1. 3.3.1. Failure to acknowledge patterns of difference and sameness in group disadvantage: The ‘anti-canon’ of intersectionality Kein Zugriff
  2. 1. 3.3.2.1. US Kein Zugriff
    2. 3.3.2.2. EU Kein Zugriff
  3. 3.3.3. Comparative discussion Kein Zugriff
4. 1. 1. 3.4.1.1. Applicable law Kein Zugriff
    2. 3.4.1.2. The uncertain status of gender identity and sexual orientation as prohibited grounds of discrimination Kein Zugriff
    3. 3.4.1.3. Affordable Care Act’s scope of protection against disparate impact in healthcare Kein Zugriff
    4. 3.4.1.4. Intersectional discrimination claims under the Affordable Care Act Kein Zugriff
    5. 1. 3.4.1.5.1. The obligations of healthcare providers to avoid discrimination Kein Zugriff
      2. 3.4.1.5.2. Algorithmic intersectional discrimination Kein Zugriff
      3. 3.4.1.5.3. The lack of extended data collection obligations Kein Zugriff
  2. 1. 3.4.2.1. The applicable law Kein Zugriff
    2. 1. 3.4.2.2.1. Towards the judicial recognition of new discrimination grounds – the role of Art. 21 of the Charter of Fundamental Rights Kein Zugriff
      2. 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 Kein Zugriff
    3. 3.4.2.3. Remedying fragmented enforcement mechanisms – towards the reform of Equality Bodies Kein Zugriff
  3. 3.4.3. Comparative discussion Kein Zugriff
5. 3.5. Conclusion Kein Zugriff
1. 4.1. Introduction Kein Zugriff
2. 1. 1. 4.2.1.1. Definition and classification of medical devices Kein Zugriff
    2. 4.2.1.2. Bias considerations in pre-market conformity assessment Kein Zugriff
    3. 4.2.1.3. Bias considerations in post-market monitoring Kein Zugriff
  2. 1. 4.2.2.1. Evidence-based DSI and Predictive DSI Kein Zugriff
    2. 4.2.2.2. Source attributes Kein Zugriff
    3. 4.2.2.3. Intervention Risk Management Kein Zugriff
  3. 4.2.3. The AI Bill of Rights Kein Zugriff
  4. 1. 4.2.4.1. The regulation of bias in foundation models Kein Zugriff
3. 1. 1. 4.3.1.1. The definition and classification of medical devices Kein Zugriff
    2. 4.3.1.2. Bias considerations in pre-marketing Kein Zugriff
    3. 4.3.1.3. Bias considerations in post-market monitoring Kein Zugriff
  2. 4.3.2. The Health Technology Assessment Regulation Kein Zugriff
  3. 1. 4.3.3.1. Risk management system and algorithmic bias Kein Zugriff
    2. 4.3.3.2. Data fairness considerations in the AI Act Kein Zugriff
    3. 4.3.3.3. Fairness-related transparency measures Kein Zugriff
    4. 4.3.3.4. Fairness-related obligations of deployers Kein Zugriff
    5. 4.3.3.5. The rights of individuals affected by algorithmic bias Kein Zugriff
    6. 4.3.3.6. The role of the fundamental rights impact assessment Kein Zugriff
    7. 4.3.3.7. Addressing algorithmic discrimination on a systemic level Kein Zugriff
    8. 4.3.3.8. The regulation of bias in foundation models Kein Zugriff
  4. 1. 4.3.4.1. The impact of the European Health Data Space on the availability of data Kein Zugriff
    2. 4.3.4.2. The impact of the European Health Data Space on the quality of data Kein Zugriff
4. 1. 4.4.1. Between the sectorial and horizontal regulation of bias in clinical AI Kein Zugriff
  2. 4.4.2. The allocation of responsibility between clinical AI providers and deployers Kein Zugriff
  3. 4.4.3. Detection and mitigation of bias in foundation models Kein Zugriff
  4. 4.4.4. Intersectional considerations in the regulation of clinical AI Kein Zugriff
5. 4.5. Conclusion Kein Zugriff
1. 5.1. Introduction Kein Zugriff
2. 1. 5.2.1. Multidimensionality and entanglement of socio-biological categories Kein Zugriff
  2. 5.2.2. Focus on historically marginalised and oppressed groups Kein Zugriff
  3. 5.2.3. Power structures on the intersection of technology, medicine and law Kein Zugriff
3. 1. 5.3.1. The contribution to intersectionality literature Kein Zugriff
  2. 5.3.2. Intersectionality wheel for clinical algorithms as a framework to develop a holistic intersectional fairness impact assessment Kein Zugriff
4. 5.4. Conclusion Kein Zugriff
Chapter 6. Conclusions Kein Zugriff Seiten 293 - 300
Bibliography Kein Zugriff Seiten 301 - 330

Intersectional Approach to Algorithmic Discrimination in Healthcare

Zusammenfassung

Publikation durchsuchen

Bibliographische Angaben

Inhaltsverzeichnis

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