Projects
2024 Working Group: Trust in Human-AI Interactions
2025-26 AI and Trust Working Group
Organization
Schwartz Reisman Institute for Technology and Society, University of Toronto
2024 Working Group
As applications of AI in health systems continue to rise, trust has emerged as an essential consideration in development and use. At the same time, trust is understood and approached in many different ways.
I brought a health systems perspective to these working groups, which examined how trust in AI was conceptualized across different disciplines.
The results of the 2024 working group were presented in an SRI webinar which you can watch on YouTube. You can also download the 2024 working group discussion paper here.
Approach
The objective of this conceptually-oriented analysis was to consider different approaches to, and understandings of, trust in human-ML systems across different disciplines associated with health systems research.
The review process was restricted to academic journal articles and involved keyword searches in Google Scholar and journal-specific searches across key disciplines. Reference checking and citation tracking of highly-cited sources was also performed, resulting in the inclusion of additional sources. Sources were selected for inclusion based on a principle of pluralism, which was intended to promote diversity of included sources across key disciplines in analysis.
Results Snapshot
Three contrasting traditions characterize multi-disciplinary perspectives on trust in human-ML interactions in public health and health care.
Cognitivist: Cognitivist approaches to trust tend to privilege the role of mental processes in understanding, promoting, or achieving trust. ML systems are understood as strictly technical systems that exist independently of, but shape and are shaped by, human thoughts and activities. Interactions between humans and ML systems are symmetrical, meaning that they can be modelled according to the influence of discrete individual, technical, or social variables.
Social-relational: Social-relational approaches to trust situate trust in relation to broader collections of actors, objects, ideas, and institutions. Trust is considered situational, contingent, and often difficult to generalize beyond specific cases. ML systems are inextricably linked to human activities and must be considered together.
Critical: Critical approaches to trust question the role, significance, and understanding of trust as a concept. While also often relational in their understandings of trust, critical approaches more explicitly situate the concept of trust, and trusting relationships, in relation to broader distributions of power and resources. Interactions between humans and ML systems are not just cognitive, or context-dependent, but examples of world-making practices that convene humans, technologies, and other objects and ideas.
Across all of these traditions are different emphases on trust in: a) ML systems themselves (e.g., software, or hardware), b) individuals who create, engage with, sponsor, or use ML systems (e.g., developers, patients, providers, policymakers, administrators), and c) health services or systems in which ML systems are embedded (e.g., primary care, tertiary care, insurance, public health, administration).
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