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Explainable AI: A Neurally-Inspired Decision Stack Framework.

Muhammad Salar KhanMehdi NayebpourMeng-Hao LiHadi El-AmineNaoru KoizumiJames L Olds
Published in: Biomimetics (Basel, Switzerland) (2022)
European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called "decision stacks" that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.
Keyphrases
  • artificial intelligence
  • big data
  • machine learning
  • deep learning
  • decision making
  • emergency department
  • gene expression
  • genome wide
  • electronic health record
  • working memory
  • data analysis