  European Law now requires AI to be explainable in the context of adverse
decisions affecting European Union (EU) citizens. At the same time, it is
expected that there will be increasing instances of AI failure as it operates
on imperfect data. This paper puts forward a neurally-inspired framework called
decision stacks that can provide for a way forward in research aimed at
developing explainable AI. Leveraging findings from memory systems in
biological brains, the decision stack framework operationalizes the definition
of explainability and then proposes a test that can potentially reveal how a
given AI decision came to its conclusion.
