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.
| Original language | English |
|---|---|
| Publication status | Accepted/In press - 1 Oct 2019 |
| Event | 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence - Tokyo, Japan Duration: 29 Oct 2019 → 1 Nov 2019 |
Workshop
| Workshop | 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence |
|---|---|
| Country/Territory | Japan |
| City | Tokyo |
| Period | 29/10/19 → 1/11/19 |
Bibliographical note
This paper started off as a (much shorter) bloghttps://ehudreiter.com/2019/07/19/nlg-and-explainable-ai/. My thanks to the people who commented on this blog, as well as the anonymous reviewers, the members of the Aberdeen CLAN research group, the members of the Explaining the Outcomes of Complex Models project at Monash, and the members of the NL4XAI research project, all of whom gave me excellent feedback and suggestions. My thanks also to Prof Rene van der Wal for his help in the experiment mentioned in section 3.