Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
Shyam Sundar
3 Pith papers cite this work. Polarity classification is still indexing.
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The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.
citing papers explorer
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Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge
Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
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Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders
The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.
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Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.