Natural Language Generation Challenges for Explainable AI

Explanations need to be purpose-driven and audience-specific: The paper emphasizes that effective AI explanations cannot be one-size-fits-all. They must be deliberately written for an explanatory purpose and tailored to their intended readers, meaning explanations for technical experts should differ fundamentally from those for end-users or regulators.

Narrative and causal structure matters for understanding: Simply listing factors that influenced an AI decision is insufficient. High-quality explanations require good narrative and causal structure, helping readers understand not just what the AI decided, but why and how different elements connected to produce that outcome.

Uncertainty and data quality must be explicitly communicated: Explanations should highlight where uncertainty exists in the AI's reasoning and acknowledge how data quality issues may have affected the output. This transparency helps users understand the limitations and reliability of AI decisions rather than treating them as definitive.

Natural Language Generation is central to responsible AI: The paper frames NLG (the AI technology that generates human-readable text) as a critical component of explainable AI, suggesting that how explanations are written is just as important as what information they contain for ensuring AI systems are truly understandable and trustworthy.