Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care
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,...
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Medical Ethics |
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| Online Access: | https://doi.org/10.1186/s12910-023-00917-w |
| _version_ | 1797795530799054848 |
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| author | Rachel Dlugatch Antoniya Georgieva Angeliki Kerasidou |
| author_facet | Rachel Dlugatch Antoniya Georgieva Angeliki Kerasidou |
| author_sort | Rachel Dlugatch |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:19:22Z |
| format | Article |
| id | doaj.art-a54b88867c974f72941f120e4f682e21 |
| institution | Directory Open Access Journal |
| issn | 1472-6939 |
| language | English |
| last_indexed | 2024-03-13T03:19:22Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Ethics |
| spelling | doaj.art-a54b88867c974f72941f120e4f682e212023-06-25T11:27:00ZengBMCBMC Medical Ethics1472-69392023-06-0124111610.1186/s12910-023-00917-wTrustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum careRachel Dlugatch0Antoniya Georgieva1Angeliki Kerasidou2Ethox Centre, Nuffield Department of Population Health, University of OxfordNuffield Department of Women’s & Reproductive Health, University of Oxford, Level 3, Women’s Centre, John Radcliffe HospitalEthox Centre, Nuffield Department of Population Health, University of OxfordAbstract 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.https://doi.org/10.1186/s12910-023-00917-wTrustTrustworthinessArtificial intelligenceEthical designPublic perceptionCardiotocography |
| spellingShingle | Rachel Dlugatch Antoniya Georgieva Angeliki Kerasidou Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care BMC Medical Ethics Trust Trustworthiness Artificial intelligence Ethical design Public perception Cardiotocography |
| title | Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care |
| title_full | Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care |
| title_fullStr | Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care |
| title_full_unstemmed | Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care |
| title_short | Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care |
| title_sort | trustworthy artificial intelligence and ethical design public perceptions of trustworthiness of an ai based decision support tool in the context of intrapartum care |
| topic | Trust Trustworthiness Artificial intelligence Ethical design Public perception Cardiotocography |
| url | https://doi.org/10.1186/s12910-023-00917-w |
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