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AI Explainability, Trust, and Calibration / Confidence Expression and Metacognitive Calibration

Users struggle to judge when AI recommendations are trustworthy and may blindly trust or over-reject them.

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users struggle to judge when AI recommendations are trustworthy and may blindly trust or over-reject them.

IUI '23Moderate Reliance on AI Recommendations: Conceptualization and the Impact of Explanations

Similar questions

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users may misjudge their own abilities due to AI confidence influence, leading to decision errors.

CHI '25As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users struggle to rely on AI appropriately; over- or under-reliance leads to decision errors.

CHI '24"Are You Really Sure?'' Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users struggle to understand robot uncertainty when collaborating on high-risk tasks with robots.

CHI '24Robot-Assisted Decision-Making: Unveiling the Role of Uncertainty Visualisation and Embodiment

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users may fail to correctly use AI recommendations due to overconfidence.

CHI '23Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users may misplaced trust or distrust automated systems, leading to misuse or abandonment.

CHI '23Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

lightbulbPractical problemConfidence Expression and Metacognitive Calibration

Users may develop over-trust in conversational agents, leading to erroneous decisions and loss of control.

CUI '22Conversational Agents Trust Calibration: A User-Centred Perspective to Design

Related papers

As Confidence Aligns: Understanding the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Jingshu Li, Yitian Yang, Q. Vera Liao

"Are You Really Sure?'' Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making

Shuai Ma, Xinru Wang, Ying Lei

Robot-Assisted Decision-Making: Unveiling the Role of Uncertainty Visualisation and Embodiment

Sarah Schömbs, Saumya Pareek, Jorge Goncalves

Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems

Gaole He, Lucie Kuiper, Ujwal Gadiraju

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

Magdalena Wischnewski, Nicole Krämer, Emmanuel Müller