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Explainable AI: A Neurally-Inspired Decision Stack Framework.

Muhammad Salar Khan1, Mehdi Nayebpour1, Meng-Hao Li1

  • 1Schar School of Policy and Government, George Mason University, Arlington, VA 22201, USA.

Biomimetics (Basel, Switzerland)
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New research introduces "decision stacks," a brain-inspired framework for developing Explainable Artificial Intelligence (X-AI). This approach aims to meet European Union explainability mandates and address AI failures from imperfect data.

Keywords:
AIdecision stackexplainable AIinterpretable AIneurally inspired

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Cognitive Science

Background:

  • European Union regulations mandate explainability for AI decisions impacting citizens.
  • Increasing AI failures are anticipated due to operation on imperfect data.
  • Existing AI systems often lack transparency in decision-making processes.

Purpose of the Study:

  • To propose a novel theoretical framework, "decision stacks," for advancing Explainable Artificial Intelligence (X-AI).
  • To operationalize the definition of explainability by drawing inspiration from biological memory systems.
  • To introduce a test for revealing the mechanisms behind AI decisions.

Main Methods:

  • Development of a neurally inspired theoretical framework named "decision stacks."
  • Leveraging findings from biological memory systems to inform the framework.
  • Operationalizing explainability and proposing a diagnostic test.

Main Results:

  • A theoretical framework, "decision stacks," is proposed for X-AI research.
  • The framework provides a method to operationalize explainability.
  • A test is suggested to investigate AI decision-making processes.

Conclusions:

  • The "decision stacks" framework offers a promising direction for developing truly Explainable Artificial Intelligence.
  • This biologically inspired approach can help meet regulatory requirements for AI transparency.
  • Further research is needed to validate the proposed test for AI explainability.