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

Malwina Anna Wójcik-Suffia

doi:10.5771/9783748971214

Search publication

Bibliographic data

Edition: 1/2026
Copyright Year: 2026
ISBN-Print: 978-3-7560-4170-1
ISBN-Online: 978-3-7489-7121-4
Publisher: Nomos, Baden-Baden
Series: Luxemburger Juristische Studien - Luxembourg Legal Studies
Volume: 27
Language: English
Pages: 331
Product Type: Monograph

1. Acknowledgements No access
2. I come to you No access
1. 1.1. Intersectional discrimination in clinical algorithms – a new old challenge? No access
2. 1.2. Objectives of the book and research questions No access
3. 1. 1.3.1. Legal informatics approach No access
  2. 1.3.2. Comparative legal approach No access
4. 1.4. Overview of the book No access
1. 2.1. Introduction No access
2. 1. 1. 2.2.1.1. The indivisibility of experience No access
    2. 2.2.1.2. The dynamics of sameness and difference in group disadvantage No access
  2. 2.2.2. The contextual strand No access
  3. 1. 2.2.3.1. Domestic and sexual violence against ethnic and racial minority women No access
    2. 2.2.3.2. Involuntary anticonception and sterilisation of ethnic and racial minority women – the illusion of choice No access
3. 1. 2.3.1. Anticategorical approach No access
  2. 2.3.2. Intracategorical approach No access
  3. 2.3.3. Intercategorical approach No access
4. 1. 2.4.1. Intersectional bias in clinical algorithms No access
  2. 2.4.2. Synthetic data as a strategy to address the shortage of intersectional data No access
  3. 1. 2.4.3.1. Fairness metrics manifestly incompatible with intersectionality No access
    2. 2.4.3.2. Towards intersectionality-sensitive fairness metrics No access
  4. 2.4.4. Power relations in intersectional fairness No access
5. 2.5. Conclusions No access
1. 3.1. Introduction No access
2. 1. 1. 3.2.1.1. Algorithmic discrimination and the theories of liability – between disparate treatment and disparate impact No access
    2. 3.2.1.2. Fairness interventions – the legality of algorithmic affirmative action No access
  2. 1. 3.2.2.1. Algorithmic discrimination and the theories of liability – between direct and indirect discrimination No access
    2. 3.2.2.2. Fairness interventions – the legality of algorithmic positive action No access
  3. 3.2.3. Comparative discussion No access
3. 1. 3.3.1. Failure to acknowledge patterns of difference and sameness in group disadvantage: The ‘anti-canon’ of intersectionality No access
  2. 1. 3.3.2.1. US No access
    2. 3.3.2.2. EU No access
  3. 3.3.3. Comparative discussion No access
4. 1. 1. 3.4.1.1. Applicable law No access
    2. 3.4.1.2. The uncertain status of gender identity and sexual orientation as prohibited grounds of discrimination No access
    3. 3.4.1.3. Affordable Care Act’s scope of protection against disparate impact in healthcare No access
    4. 3.4.1.4. Intersectional discrimination claims under the Affordable Care Act No access
    5. 1. 3.4.1.5.1. The obligations of healthcare providers to avoid discrimination No access
      2. 3.4.1.5.2. Algorithmic intersectional discrimination No access
      3. 3.4.1.5.3. The lack of extended data collection obligations No access
  2. 1. 3.4.2.1. The applicable law No access
    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 No access
      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 No access
    3. 3.4.2.3. Remedying fragmented enforcement mechanisms – towards the reform of Equality Bodies No access
  3. 3.4.3. Comparative discussion No access
5. 3.5. Conclusion No access
1. 4.1. Introduction No access
2. 1. 1. 4.2.1.1. Definition and classification of medical devices No access
    2. 4.2.1.2. Bias considerations in pre-market conformity assessment No access
    3. 4.2.1.3. Bias considerations in post-market monitoring No access
  2. 1. 4.2.2.1. Evidence-based DSI and Predictive DSI No access
    2. 4.2.2.2. Source attributes No access
    3. 4.2.2.3. Intervention Risk Management No access
  3. 4.2.3. The AI Bill of Rights No access
  4. 1. 4.2.4.1. The regulation of bias in foundation models No access
3. 1. 1. 4.3.1.1. The definition and classification of medical devices No access
    2. 4.3.1.2. Bias considerations in pre-marketing No access
    3. 4.3.1.3. Bias considerations in post-market monitoring No access
  2. 4.3.2. The Health Technology Assessment Regulation No access
  3. 1. 4.3.3.1. Risk management system and algorithmic bias No access
    2. 4.3.3.2. Data fairness considerations in the AI Act No access
    3. 4.3.3.3. Fairness-related transparency measures No access
    4. 4.3.3.4. Fairness-related obligations of deployers No access
    5. 4.3.3.5. The rights of individuals affected by algorithmic bias No access
    6. 4.3.3.6. The role of the fundamental rights impact assessment No access
    7. 4.3.3.7. Addressing algorithmic discrimination on a systemic level No access
    8. 4.3.3.8. The regulation of bias in foundation models No access
  4. 1. 4.3.4.1. The impact of the European Health Data Space on the availability of data No access
    2. 4.3.4.2. The impact of the European Health Data Space on the quality of data No access
4. 1. 4.4.1. Between the sectorial and horizontal regulation of bias in clinical AI No access
  2. 4.4.2. The allocation of responsibility between clinical AI providers and deployers No access
  3. 4.4.3. Detection and mitigation of bias in foundation models No access
  4. 4.4.4. Intersectional considerations in the regulation of clinical AI No access
5. 4.5. Conclusion No access
1. 5.1. Introduction No access
2. 1. 5.2.1. Multidimensionality and entanglement of socio-biological categories No access
  2. 5.2.2. Focus on historically marginalised and oppressed groups No access
  3. 5.2.3. Power structures on the intersection of technology, medicine and law No access
3. 1. 5.3.1. The contribution to intersectionality literature No access
  2. 5.3.2. Intersectionality wheel for clinical algorithms as a framework to develop a holistic intersectional fairness impact assessment No access
4. 5.4. Conclusion No access
Chapter 6. Conclusions No access Pages 293 - 300
Bibliography No access Pages 301 - 330