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Cited by 63Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care

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Dlugatch, Rachel, Georgieva, Antoniya, Kerasidou, Angeliki·2023·BMC Medical Ethics
DOI 10.1186/s12910-023-00917-wPMC PMC10283298
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Abstract

Background: Despite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness. Methods: Seventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI. Results: Three themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI. Conclusions: The ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

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Cited by

Sorted by citations

Authors
Dlugatch, R., Georgieva, A., & Kerasidou, A.
Affiliations
  • University of Oxford (GB)
  • John Radcliffe Hospital (GB)
Corresponding author
Kerasidou, Angeliki
ORCID
Venue
BMC Medical Ethics
Publisher
Springer Science and Business Media LLC
Published
2023-06-20
Volume
24
Issue
1
Pages
42-42
ISSN
1472-6939 (ISSN-L)
DOI
10.1186/s12910-023-00917-w
PMC
PMC10283298
OpenAlex
W4381434199
Cited by
63
References
7
Citations by year
2026: 19 · 2025: 23 · 2024: 20 · 2023: 1
FWCI
2.34
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Ethics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Oxford University Research Archive (ORA) (University of Oxford) (submittedVersion, cc-by)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
UK National Institute of Health and Care Research (NIHR202117)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
trustworthy AI, ethical design, public perception of AI, intrapartum care, cardiotocography, AI-based decision support, trustworthiness, birth parents, mothers, data representativeness, patient-centered care
MeSH terms
Artificial Intelligence, England, Female, Humans, Pregnancy, Public Opinion, Reproducibility of Results, Acceptance and Commitment Therapy

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