Vulnerability intersections: an integrative conceptual framework for understanding vulnerability in AI-assisted care for older adults.
7 affiliations
- 1Department of Health Sciences, Lund University, Lund, Sweden.
- 2Department of Law, Tallinn University of Technology, Tallinn, Estonia.
- 3Department of Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
- 4Department of Interdisciplinary Studies of Culture, Norwegian University of Science and Technology, Trondheim, Norway.
- 5Department of Social Sciences, University of Eastern Finland, Kuopio, Finland.
- 6IT College, Tallinn University of Technology, Tallinn, Estonia.
- 7Department of Design Sciences, Lund University, Lund, Sweden.
Abstract
Artificial Intelligence (AI) applications are increasingly used to support independent living and care for older adults, particularly those in vulnerable situations. Yet, how vulnerability is constructed when AI enters later-life care remains poorly theorized. Existing approaches to vulnerability in old age inadequately address AI-related risks, structural conditions, exposure pathways, and life course disadvantages. Using a theory-synthesis approach, this article proposes "vulnerability intersections" as an integrative framework for how vulnerability is constructed, enacted, and negotiated in AI-assisted care for older adults. The framework extends existing sociotechnical accounts by conceptualizing vulnerability as taking four overlapping forms: anticipatory, opaque, routinized, and normative. It locates vulnerability factors at the micro level of older adults' lives, the meso level of care organizations and technology providers, and the macro level of political-economic and regulatory arrangements, with life-course accumulation spanning all three. It also proposes three exposure pathways through which vulnerability factors become consequential: AI-mediated information and representations of aging; AI-powered care organization and delivery; and AI-configured power, participation, and social relations. The framework shifts attention away from viewing older adults as inherently vulnerable toward examining the sociotechnical arrangements under which vulnerability is anticipated, classified, distributed, and negotiated when AI enters care.
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BibTeX
@article{xu2026vulnerability,
title = {{Vulnerability intersections: an integrative conceptual framework for understanding vulnerability in AI-assisted care for older adults.}},
author = {Wenqian Xu and Maria Claudia Solarte Vasquez and Aud Uhlen Obstfelder and Roger Andre Søraa and Jin Kristian Hurum and Xingyue Su and Honglin Chen and Katrin Kangur and Susanne Frennert},
journal = {The Gerontologist},
year = {2026},
month = oct,
doi = {10.1093/geront/gnag239},
url = {https://europepmc.org/article/MED/42844578},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Vulnerability intersections: an integrative conceptual framework for understanding vulnerability in AI-assisted care for older adults.
AU - Wenqian Xu
AU - Maria Claudia Solarte Vasquez
AU - Aud Uhlen Obstfelder
AU - Roger Andre Søraa
AU - Jin Kristian Hurum
AU - Xingyue Su
AU - Honglin Chen
AU - Katrin Kangur
AU - Susanne Frennert
PY - 2026
DA - 2026/10/01
JO - The Gerontologist
DO - 10.1093/geront/gnag239
UR - https://europepmc.org/article/MED/42844578
AB - Artificial Intelligence (AI) applications are increasingly used to support independent living and care for older adults, particularly those in vulnerable situations. Yet, how vulnerability is constructed when AI enters later-life care remains poorly theorized. Existing approaches to vulnerability in old age inadequately address AI-related risks, structural conditions, exposure pathways, and life course disadvantages. Using a theory-synthesis approach, this article proposes "vulnerability intersections" as an integrative framework for how vulnerability is constructed, enacted, and negotiated in AI-assisted care for older adults. The framework extends existing sociotechnical accounts by conceptualizing vulnerability as taking four overlapping forms: anticipatory, opaque, routinized, and normative. It locates vulnerability factors at the micro level of older adults' lives, the meso level of care organizations and technology providers, and the macro level of political-economic and regulatory arrangements, with life-course accumulation spanning all three. It also proposes three exposure pathways through which vulnerability factors become consequential: AI-mediated information and representations of aging; AI-powered care organization and delivery; and AI-configured power, participation, and social relations. The framework shifts attention away from viewing older adults as inherently vulnerable toward examining the sociotechnical arrangements under which vulnerability is anticipated, classified, distributed, and negotiated when AI enters care.
SN - 0016-9013
SN - 1758-5341
ER -