Explainable AI: A Neurally-Inspired Decision Stack Framework
Muhammad Salar Khan, Mehdi Nayebpour, Menghao Li, Hadi El‐Amine, Naoru Koizumi, John Olds
Abstract
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.
Topics & Concepts
Stack (abstract data type)Computer scienceArtificial intelligenceProgramming languageExplainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningMachine Learning in Healthcare