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Health·P Space·Evidence-backed problem·Published 2026-08-08

Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Abstract: Artificial intelligence (AI) in healthcare is assumed to introduce risks that are not easily addressed by dominant philosophical models for thinking about responsibility. When an AI tool makes an error that results in patient harm, the question of who is responsible is rarely straightforward. Dominant models of responsibility work when harm can be traced to a single actor, but they fail in socio-technical systems where decisions and actions are distributed across multiple human and technological agents. Iris Mar…

TRV-2026-0686Peer-reviewedPermanent record — cite & verify
Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Economic evaluation of Voice Recognition (VR) the clinicians' desktop at the Naval Hospital Roosevelt Roads (NHRR) by Threet, Erik.. Public domain

The quick read

The peer-reviewed article examines responsibility gaps when AI tools in healthcare cause patient harm. It notes that traditional models struggle because decisions are spread across clinicians, developers, institutions and the AI systems themselves.

It introduces Iris Marion Young's social connection model as a way to allocate differentiated, forward-looking responsibilities based on power, privilege, interest, collective ability and personal connection, rather than only assigning blame after harm occurs. The piece is conceptual and does not report measured outcomes of implementing the model.

Main points
  • AI-driven healthcare distributes decisions across multiple human and technological agents, complicating attribution of responsibility after patient harm.
  • Dominant liability models fail when harm cannot be traced to a single actor in socio-technical systems.
  • Young's social connection model proposes forward-looking responsibility tied to participation in structural processes that produce harm.
  • Framework differentiates responsibilities using parameters of power, privilege, interest, collective ability, and personal connection.
Problem

AI tools used in healthcare can make errors that result in patient harm, with responsibility difficult to assign because decisions are distributed across human and technological agents.

The rundown

The article argues that when an AI tool makes an error that results in patient harm, dominant models that trace harm to a single actor break down in socio-technical systems where actions are distributed.

It presents Iris Marion Young's social connection model as a forward-looking alternative that attaches responsibility to those who participate in and benefit from structural processes, organized around power, privilege, interest, collective ability, and personal connection.

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