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Summary
This summary is machine-generated.

Encouraging skepticism towards artificial intelligence (AI) advice does not improve performance and may worsen it. Trust in AI increases while distrust decreases, even as AI accuracy declines, highlighting complex human-AI interaction dynamics.

Keywords:
Bayesian parameter estimationSignal Detection Theorydistrusthuman-AI interactionimage classificationtrusttrust in AI

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Area of Science:

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

Background:

  • Trustworthy artificial intelligence (AI) is crucial in high-risk fields like medicine.
  • Current research often focuses on fostering user trust in AI for better performance.
  • However, the ability to critically review and distrust AI decisions is also vital.

Purpose of the Study:

  • To investigate the impact of skepticism instructions on human-AI interaction performance.
  • To analyze how trust and distrust in AI evolve as AI performance degrades.
  • To quantify appropriate reliance using Bayesian Signal Detection Theory.

Main Methods:

  • Two image classification scenarios with mock AI advice were created.
  • Participants received AI advice of decreasing quality.
  • Task performance, trust, and distrust were measured, with conditions including a skepticism instruction versus a neutral one.

Main Results:

  • Skepticism instructions did not improve, and in fact worsened, participant performance.
  • Trust in AI increased and distrust decreased as AI quality declined, irrespective of instructions.
  • Bayesian Signal Detection Theory analysis provided a method to assess appropriate reliance.

Conclusions:

  • Instructing users to be skeptical of AI does not enhance performance and may be detrimental.
  • User trust dynamics with AI are complex, showing increased trust despite declining accuracy.
  • Findings have implications for AI disclaimers and future research on trust and distrust in AI systems.