A Definition of Good Explanations and the Challenges Explaining LLM Outputs

2026-06-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Published on 2026-06-12, a new paper introduces a definition for "good explanations" specifically tailored for Artificial Intelligence outputs. This definition builds upon the concept of counterfactual explanations, which identify what minimal changes to inputs would alter an outcome. Crucially, the proposed framework extends this by asserting that a good explanation must also account for the interlocutor's pre-existing beliefs regarding each fact presented. The research then investigates the implications of this refined definition for AI explainability, highlighting why generating effective explanations for Large Language Model (LLM) outputs poses significant difficulties. The work emphasizes that understanding the recipient's prior knowledge is essential for producing truly comprehensible and useful AI explanations.

Key takeaway

For AI Scientists and Ethicists designing or evaluating explainable AI systems, particularly for LLMs, you must move beyond basic counterfactuals. Your explanation frameworks should explicitly model and account for the end-user's prior beliefs about the system's facts and reasoning. This shift is critical for developing truly "good" explanations that are both comprehensible and trustworthy, rather than merely technically accurate. Prioritize user-centric belief modeling to overcome inherent LLM explainability hurdles.

Key insights

Good AI explanations combine counterfactuals with the interlocutor's prior beliefs to address LLM explainability challenges.

Principles

• Explanations must incorporate counterfactual reasoning.
• Interlocutor's prior beliefs define explanation quality.
• LLM outputs present unique explainability challenges.

Topics

• Explainable AI
• Large Language Models
• Counterfactual Explanations
• Explanation Quality
• Prior Beliefs

Best for: Research Scientist, AI Scientist, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.