Natural Language Generation Challenges for Explainable AI
arXiv:1911.08794
Abstract
Good quality explanations of artificial intelligence (XAI) reasoning must be written (and evaluated) for an explanatory purpose, targeted towards their readers, have a good narrative and causal structure, and highlight where uncertainty and data quality affect the AI output. I discuss these challenges from a Natural Language Generation (NLG) perspective, and highlight four specific NLG for XAI research challenges.
Presented at the NL4XAI workshop (https://sites.google.com/view/nl4xai2019/)