TY - CONF
T1 - Natural Language Generation Challenges for Explainable AI
AU - Reiter, Ehud B.
N1 - This paper started off as a (much shorter) blog
https://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.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
M3 - Unpublished paper
T2 - 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
Y2 - 29 October 2019 through 1 November 2019
ER -