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Area of Science:

  • Cognitive Psychology
  • Artificial Intelligence
  • Explainable AI (XAI)

Background:

  • Deep neural networks in artificial intelligence (AI) present a

Purpose of the Study:

  • To explore the contributions of cognitive psychology to the emerging field of explainable artificial intelligence (XAI).
  • To bridge the gap between cognitive science and AI by applying experimental methods to understand AI decision-making.
  • To address the 'black-box' problem in AI, particularly in applications impacting human well-being.

Main Methods:

  • Reviewing current XAI methodologies and identifying a 'blind spot' addressable by cognitive psychology.
  • Proposing a framework for integrating experimental cognitive psychology approaches into XAI research.
  • Providing a tutorial on applying experimental methods to study AI systems.

Main Results:

  • Current XAI methods can be significantly enhanced by the experimental rigor and modeling techniques from cognitive psychology.
  • An experimental approach offers unique advantages for improving AI interpretability, fairness, and transparency.
  • Psychological science principles can guide the development of more effective XAI techniques.

Conclusions:

  • Cognitive psychologists are well-positioned to contribute to XAI due to their extensive experience in modeling complex systems (the human mind).
  • Adopting experimental methods from psychology can overcome limitations in current XAI approaches.
  • There is a call for increased interdisciplinary research between cognitive psychology and AI to advance explainable AI.