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A tale of two explanations: Enhancing human trust by explaining robot behavior.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Explainability is crucial for AI and robot acceptance in critical applications.
  • Current AI systems often lack comprehensive explanations for their actions.
  • Human trust in intelligent systems is essential for widespread adoption.

Purpose of the Study:

  • To investigate which explanation formats most effectively build human trust in AI and robot systems.
  • To propose a framework for generating explanations from both functional and mechanistic viewpoints.
  • To explore the relationship between explanation effectiveness and task performance.

Main Methods:

  • A robot system learned to open medicine bottles from human demonstrations.
  • Utilized an embodied haptic prediction model for sensory feedback knowledge extraction.
  • Employed a stochastic grammar model for task compositional structure and an improved Earley parsing algorithm.
  • Conducted a psychological experiment comparing different explanation types (visualizations vs. text summaries).

Main Results:

  • The robot successfully learned and executed the bottle-opening task on unseen bottles.
  • Real-time, comprehensive visualizations of the robot's internal decisions significantly enhanced human trust compared to text summaries.
  • Explanation forms that best foster trust do not always align with components that optimize task performance.

Conclusions:

  • Integrating diverse model components is necessary to simultaneously improve task execution and human trust in AI and robotics.
  • Visual, real-time explanations are more effective for building trust than static text descriptions.
  • A gap exists between optimizing task performance and optimizing human trust through explanations.