The definition of good explanations has been a long-standing philosophical debate, recently reignited in the context of AI outputs. Explainability is crucial for AI adoption in many contexts, but to produce good explanations of AI systems, we must first understand what constitutes good explanations. This paper proposes a definition inspired by counterfactual explanations, arguing that one must also consider the interlocutor's prior beliefs regarding each fact that could be presented in an explanation. We explore the implications of this definition for AI explainability and particularly why good explanations for LLM outputs are challenging to produce.
Blogger's Review: This paper delves into the significance of AI explainability and emphasizes the need for personalized explanations tailored to different audiences. This insight is crucial for enhancing transparency and trust in AI technologies.