Explainable AI: A Neurally-Inspired Decision Stack Framework
This addresses the need for explainable AI in legal contexts, particularly for EU citizens affected by adverse decisions, but it appears incremental as it builds on existing neural and memory system concepts without demonstrating concrete results.
The paper tackles the problem of making AI decisions explainable to comply with European Law, proposing a neurally-inspired decision stack framework that operationalizes explainability and introduces a test to reveal AI decision-making processes.
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.