TY - JOUR
T1 - Navigating artificial intelligence in care homes
T2 - Competing stakeholder views of trust and logics of care
AU - Neves, Barbara Barbosa
AU - Omori, Maho
AU - Petersen, Alan
AU - Vered, Mor
AU - Carter, Adrian
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/10
Y1 - 2024/10
N2 - The COVID-19 pandemic shed light on systemic issues plaguing care (nursing) homes, from staff shortages to substandard healthcare. Artificial Intelligence (AI) technologies, including robots and chatbots, have been proposed as solutions to such issues. Yet, socio-ethical concerns about the implications of AI for health and care practices have also been growing among researchers and practitioners. At a time of AI promise and concern, it is critical to understand how those who develop and implement these technologies perceive their use and impact in care homes. Combining a sociological approach to trust with Annemarie Mol's logic of care and Jeanette Pol's concept of fitting, we draw on 18 semi-structured interviews with care staff, advocates, and AI developers to explore notions of human-AI care. Our findings show positive perceptions and experiences of AI in care homes, but also ambivalence. While integrative care incorporating humans and technology was salient across interviewees, we also identified experiential, contextual, and knowledge divides between AI developers and care staff. For example, developers lacked experiential knowledge of care homes' daily functioning and constraints, influencing how they designed AI. Care staff demonstrated limited experiential knowledge of AI or more critical views about contexts of use, affecting their trust in these technologies. Different understandings of ‘good care’ were evident, too: ‘warm’ care was sometimes linked to human care and ‘cold’ care to technology. In conclusion, understandings and experiences of AI are marked by different logics of sociotechnical care and related levels of trust in these sensitive settings.
AB - The COVID-19 pandemic shed light on systemic issues plaguing care (nursing) homes, from staff shortages to substandard healthcare. Artificial Intelligence (AI) technologies, including robots and chatbots, have been proposed as solutions to such issues. Yet, socio-ethical concerns about the implications of AI for health and care practices have also been growing among researchers and practitioners. At a time of AI promise and concern, it is critical to understand how those who develop and implement these technologies perceive their use and impact in care homes. Combining a sociological approach to trust with Annemarie Mol's logic of care and Jeanette Pol's concept of fitting, we draw on 18 semi-structured interviews with care staff, advocates, and AI developers to explore notions of human-AI care. Our findings show positive perceptions and experiences of AI in care homes, but also ambivalence. While integrative care incorporating humans and technology was salient across interviewees, we also identified experiential, contextual, and knowledge divides between AI developers and care staff. For example, developers lacked experiential knowledge of care homes' daily functioning and constraints, influencing how they designed AI. Care staff demonstrated limited experiential knowledge of AI or more critical views about contexts of use, affecting their trust in these technologies. Different understandings of ‘good care’ were evident, too: ‘warm’ care was sometimes linked to human care and ‘cold’ care to technology. In conclusion, understandings and experiences of AI are marked by different logics of sociotechnical care and related levels of trust in these sensitive settings.
KW - AI
KW - Aged care
KW - Artificial intelligence
KW - Long-term care
KW - Nursing homes
KW - Older people
KW - Qualitative research
KW - Sociotechnical care
KW - Stakeholders
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85201418722&partnerID=8YFLogxK
U2 - 10.1016/j.socscimed.2024.117187
DO - 10.1016/j.socscimed.2024.117187
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C2 - 39173291
AN - SCOPUS:85201418722
SN - 0277-9536
VL - 358
JO - Social Science and Medicine
JF - Social Science and Medicine
M1 - 117187
ER -