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Stakeholder Criteria for Trust in Artificial Intelligence-Based Computer Perception Tools in Health Care: Qualitative

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Trust in computer perception (CP) technologies for mental health relies on more than just performance. Factors like alignment with beliefs and expert endorsement significantly influence stakeholder trust, highlighting potential biases.

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Area of Science:

  • Digital Mental Health
  • Human-Computer Interaction
  • Clinical Informatics

Background:

  • Computer perception (CP) technologies analyze passive sensing data for mental health insights.
  • Successful implementation of CP in healthcare hinges on establishing stakeholder trust.

Purpose of the Study:

  • Investigate trust factors for CP technologies among adolescents, caregivers, clinicians, and developers.
  • Identify conditions under which CP tools are considered trustworthy in healthcare settings.

Main Methods:

  • Conducted 80 semistructured interviews with diverse stakeholder groups (adolescents, caregivers, clinicians, developers).
  • Utilized multistage, inductive thematic content analysis for qualitative data interpretation.

Main Results:

  • Identified five trust prerequisites: epistemic alignment, demonstrable rigor, explainability, sensitivity to complexity, and a nonsubstitutive role.
  • Epistemic alignment (output consistency with beliefs) emerged as a dominant, yet potentially biasing, trust factor.
  • Relational trust via endorsements was observed, but raises concerns about misapplied trust without validation.

Conclusions:

  • Trust in AI-based CP technologies is shaped by cognitive heuristics, social factors, and epistemological alignment, not solely technical performance.
  • Recommends institutional frameworks for validation/oversight and clinician training to mitigate bias and ensure critical appraisal.
  • Essential to recalibrate trust towards actual system capacities for ethical and effective CP integration in mental healthcare.