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Adaptive trust calibration for human-AI collaboration.

Kazuo Okamura1, Seiji Yamada1,2

  • 1Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan.

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Humans can improve their trust in AI systems by using adaptive trust calibration cues. These cues help prevent over-trusting autonomous systems, enhancing safety and efficiency in human-AI collaboration.

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Safety
  • Cognitive Psychology

Background:

  • Human-AI collaboration effectiveness hinges on appropriate trust calibration.
  • Over-trusting autonomous systems poses significant safety risks.
  • Limited research exists on detecting and mitigating improper trust calibration.

Purpose of the Study:

  • To propose and evaluate a method for adaptive trust calibration.
  • To develop a framework for detecting inappropriate trust calibration status.
  • To introduce "trust calibration cues" to prompt users for recalibration.

Main Methods:

  • An online experiment using a drone simulator.
  • Monitoring user reliance behavior and cognitive cues.
  • Evaluating four types of trust calibration cues.

Main Results:

  • Adaptive presentation of simple cues significantly promoted trust calibration.
  • The proposed framework effectively detected inappropriate trust calibration.
  • Participants adjusted their reliance based on presented cues.

Conclusions:

  • Adaptive trust calibration is crucial for safe and efficient human-AI collaboration.
  • Trust calibration cues can effectively mitigate over-trust in AI.
  • The developed framework offers a practical approach to managing trust in autonomous systems.