Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice.
Explainable AI
Explainable reasoning
MHealth
Natural Language Generation
Ontologies
Journal
Artificial intelligence in medicine
ISSN: 1873-2860
Titre abrégé: Artif Intell Med
Pays: Netherlands
ID NLM: 8915031
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
30
09
2019
revised:
21
01
2020
accepted:
27
02
2020
entrez:
8
6
2020
pubmed:
9
6
2020
medline:
19
8
2021
Statut:
ppublish
Résumé
Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behavior. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: (i) the Natural Language Generation of messages that explain the reasoner inconsistency; and, (ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users' behaviors.
Identifiants
pubmed: 32505427
pii: S0933-3657(19)31014-0
doi: 10.1016/j.artmed.2020.101840
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101840Informations de copyright
Copyright © 2020. Published by Elsevier B.V.