Abstract
This paper engages with a key debate surrounding artificial intelligence in health and medicine, with an emphasis on women’s healthcare. In particular, the paper seeks to capture the lack of gender parity where women’s health is concerned, a consequence of systemic biases and discrimination in both historical and contemporary medical and health data. The existing literature review demonstrates that there is not only a gender data gap in AI technologies and data science fields—but there is also a gender data gap in women’s healthcare that results in algorithmic gender bias, impacting negatively on women’s healthcare experiences, treatment protocols, and finally, rights in health. On this basis, the article seeks to offer a concise exploration of the gender-related aspects of medicine and healthcare, shedding light on the biases encountered by women in the context of AI-driven healthcare. Subsequently, it conducts a doctrinal comparative law examination of the existing legislative landscape to scrutinise whether current supranational AI regulations or legal frameworks explicitly encompass the protection of fundamental rights for female patients in the realm of health AI. The scope of this analysis encompasses the legal framework governing AI-driven technologies within the European Union (EU), the Council of Europe (CoE), and, to a limited extent, the United Kingdom (UK). Lastly, this paper explores the potential utility of data feminism (that draws on intersectionality theory) as an additional tool for advancing gender equity in healthcare.
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Notes
- 1.
Colombo and Sanguineti (2018).
- 2.
Vlamos et al. (2013).
- 3.
Copeland et al. (2020).
- 4.
‘European Health Data Space’ (12 May 2023) <https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en> accessed on 16 September 2023.
- 5.
Lee and Yoon (2021).
- 6.
Cleghorn (2021).
- 7.
Criado-Perez (2020).
- 8.
Danesi (2021).
- 9.
D’Ignazio and Klein (2020).
- 10.
Kushner (2008).
- 11.
British Medical Association (2021).
- 12.
Ibid., 1.
- 13.
British Medical Association and Jewett (2021).
- 14.
Ibid., 3.
- 15.
United Nations, ‘DigitALL: Innovation and Technology for Gender Equality - UN Observance of International Women’s Day 2023 | UN Web TV’ (UN Web TV, 8 March 2023) <https://media.un.org/en/asset/k1m/k1mpovmzy5> accessed on 12 March 2023.
- 16.
‘In Focus: International Women’s Day’ (UN Women – Headquarters, 1 March 2023) <https://www.unwomen.org/en/news-stories/in-focus/2023/03/in-focus-international-womens-day> accessed on 13 March 2023.
- 17.
Cumberlege (2020).
- 18.
Ibid.
- 19.
MBRRACE-UK et al. (2020).
- 20.
Ibid.
- 21.
Lan (2012), p. 166.
- 22.
Paxling (2019).
- 23.
Merone et al. (2022), p. 49.
- 24.
Merone (2022), p. 6.
- 25.
Haraway (1997).
- 26.
Harding (2009), p. 192.
- 27.
Barad (2007).
- 28.
Butler (2011).
- 29.
Young et al. (2019), p. 337.
- 30.
‘Lack of Females in Drug Dose Trials Leads to Overmedicated Women: Gender Gap Leaves Women Experiencing Adverse Drug Reactions Nearly Twice as Often as Men, Study Shows’ (ScienceDaily) <https://www.sciencedaily.com/releases/2020/08/200812161318.htm> accessed on 13 March 2023.
- 31.
Zucker and Prendergast (2020), p. 32.
- 32.
Ibid.
- 33.
Merone et al. (2022).
- 34.
Ibid.
- 35.
‘Lack of Females in Drug Dose Trials Leads to Overmedicated Women: Gender Gap Leaves Women Experiencing Adverse Drug Reactions Nearly Twice as Often as Men, Study Shows’ (n 30).
- 36.
‘A Union of Equality: Gender Equality Strategy 2020-2025 | Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions’ 5.
- 37.
Cleghorn (2021).
- 38.
Hutchison (2020), p. 236.
- 39.
Handley et al. (2015), p. 13201.
- 40.
Tomasz Müldner, ‘An Algorithm for Explaining Algorithms’, 30.
- 41.
Manyika (2019).
- 42.
Westervelt (2015).
- 43.
Amoore (2020).
- 44.
Ibid.
- 45.
Ibid., 8.
- 46.
Burrell (2020), p. 410.
- 47.
Ibid.
- 48.
Ibid., 411.
- 49.
Lau et al. (2023), p. 2.
- 50.
Ibid., 4.
- 51.
Ibid., 7.
- 52.
Ibid., 12.
- 53.
Deziel (2016).
- 54.
Gerke et al. (2020), p. 304.
- 55.
Fosch-Villaronga et al. (2022), p. 105735.
- 56.
Cerit et al. (2020), p. 100009.
- 57.
‘Science and Technology Workforce: Women in Majority - Products Eurostat News - Eurostat’ <https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20230602-1> accessed on 16 September 2023.
- 58.
‘A Union of Equality: Gender Equality Strategy 2020-2025 | Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions’ (n 37).
- 59.
Ibid., 9.
- 60.
Ibid., 10.
- 61.
Smith and Rustagi (2021).
- 62.
Alake (2020).
- 63.
UN General Assembly, ‘Convention on the Elimination of All Forms of Discrimination against Women’ (1979) 20, retrieved in April 2006 <https://www.files.ethz.ch/isn/125360/8009_UN_Convention_Discrimination_Women.pdf> accessed on 31 May 2017.
- 64.
Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS 2021 ({SEC(2021) 167 final} - {SWD(2021) 84 final} - {SWD(2021) 85 final}).
- 65.
‘Regulatory Framework Proposal on Artificial Intelligence | Shaping Europe’s Digital Future’ (6 February 2023) <https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai> accessed on 16 March 2023.
- 66.
Article 3, EU AI Act.
- 67.
Articles 26 to 28, EU AI Act.
- 68.
‘REGULATION (EU) 2016/ 679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL - of 27 April 2016 - on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/ 46/ EC (General Data Protection Regulation)’ 88.
- 69.
Mahler (2021).
- 70.
Edwards (2022).
- 71.
REGULATION (EU) 2017/ 745 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL - of 5 April 2017 - on medical devices, amending Directive 2001/ 83/ EC, Regulation (EC) No 178/ 2002 and Regulation (EC) No 1223/ 2009 and repealing Council Directives 90/ 385/ EEC and 93/ 42/ EEC 175.
- 72.
REGULATION (EU) 2017/746 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 5 April 2017 on in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU 2016.
- 73.
Palmieri and Goffin (2023).
- 74.
Butcher (2023).
- 75.
‘A Pro-Innovation Approach to AI Regulation’ (GOV.UK) <https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper> accessed on 15 September 2023.
- 76.
‘Translation: Measures for the Management of Generative Artificial Intelligence Services (Draft for Comment) – April 2023’ (DigiChina) <https://digichina.stanford.edu/work/translation-measures-for-the-management-of-generative-artificial-intelligence-services-draft-for-comment-april-2023/> accessed on 15 September 2023.
- 77.
Raposo (2022).
- 78.
Revised Zero Draft Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law 2023 (CAI (2023) 01).
- 79.
Article 3 of the CoE AI Convention reads: ‘The implementation of the provisions of this Convention by the Parties shall be secured without discrimination on any ground such as sex, gender, sexual orientation, race, colour, language, age, religion, political or any other opinion, national or social origin, association with a national minority, property, birth, state of health, disability or other status, or based on a combination of one or more of these grounds’.
- 80.
Revised Zero Draft Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law.
- 81.
Committee on Artificial Intelligence, ‘CAI(2023)18 - Consolidated Working Draft Framework Convention on AI, Human Rights, Democracy and the Rule of Law’.
- 82.
‘A Pro-Innovation Approach to AI Regulation’ (n 76).
- 83.
Ibid., 9.
- 84.
‘Regulating AI in the UK’ <https://www.adalovelaceinstitute.org/report/regulating-ai-in-the-uk/> accessed on 15 September 2023.
- 85.
Ibid.
- 86.
‘A Pro-Innovation Approach to AI Regulation – Law Society Response’ (The Law Society, 27 June 2023) <https://www.lawsociety.org.uk/campaigns/consultation-responses/a-pro-innovation-approach-to-ai-regulation> accessed on 15 September 2023.
- 87.
Foster and Badger (2023).
- 88.
D’Ignazio and Klein (2020).
- 89.
Crenshaw (1989).
- 90.
Cole (2009).
- 91.
D’Ignazio and Klein (2020).
- 92.
Forrest (2019).
- 93.
Dórea and Revie (2021).
- 94.
Rascouet-Paz (2020).
- 95.
Alake (2020).
- 96.
Welten et al. (2022).
- 97.
Baah et al. (2019), p. 12268.
- 98.
Lau et al. (2023).
- 99.
Darian et al. (2023).
- 100.
Kelly et al. (2021).
- 101.
Meslin (2010).
- 102.
Pidun et al. (2021).
- 103.
Ibid.
- 104.
Hillman (2020).
- 105.
Shai et al. (2021).
- 106.
Rao (2016).
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Lau, P.L. (2024). AI Gender Biases in Women’s Healthcare: Perspectives from the United Kingdom and the European Legal Space. In: Gill-Pedro, E., Moberg, A. (eds) YSEC Yearbook of Socio-Economic Constitutions 2023. YSEC Yearbook of Socio-Economic Constitutions, vol 2023. Springer, Cham. https://doi.org/10.1007/16495_2023_63
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