Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals

被引:1
|
作者
Wang, Weisha [1 ]
Wang, Yichuan [2 ]
Chen, Long [3 ]
Ma, Rui [4 ]
Zhang, Minhao [5 ]
机构
[1] Soochow Univ, Res Ctr Smarter Supply Chain, Business Sch, 50 Donghuan Rd, Suzhou 215006, Peoples R China
[2] Univ Sheffield, Sheffield Univ Management Sch, Conduit Rd, Sheffield S10 1FL, England
[3] Brunel Univ London, London, England
[4] Univ Greenwich, Greenwich Business Sch, London, England
[5] Univ Bristol, Univ Bristol Sch Management, Bristol, England
基金
中国国家自然科学基金;
关键词
Handling Editor: Susan J. Elliott; Felt accountability; Artificial Intelligence (AI); Ethical principles; fsQCA; Healthcare; AI; ETHICS; TRUST;
D O I
10.1016/j.socscimed.2024.116717
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals' sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals.
引用
收藏
页数:11
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