research-article

Explaining reasoning algorithms with persuasiveness: a case study for a behavioural change system

Authors:

Ivan Donadello,

Claudio EccherAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 646 - 653

https://doi.org/10.1145/3341105.3373910

Published: 30 March 2020 Publication History

Abstract

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 behaviour. 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; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users' behaviours.

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Cited By

Suffian M(2024)Explainable AI Assisted Decision-Making and Human BehaviourComputing, Internet of Things and Data Analytics10.1007/978-3-031-53717-2_36(376-385)Online publication date: 22-Feb-2024
https://doi.org/10.1007/978-3-031-53717-2_36
Engelmann DFerrando APanisson AAncona DBordini RMascardi V(2022)RV4JaCa – Runtime Verification for Multi-Agent SystemsElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.362.5362(23-36)Online publication date: 20-Jul-2022
https://doi.org/10.4204/EPTCS.362.5
Engelmann DDamasio JPanisson AMascardi VBordini R(2022)Argumentation as a Method for Explainable AI : A Systematic Literature Review2022 17th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI54924.2022.9820411(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.23919/CISTI54924.2022.9820411
Show More Cited By

Index Terms

Explaining reasoning algorithms with persuasiveness: a case study for a behavioural change system
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
    2. Natural language processing
      1. Natural language generation

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Information

Published In

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Suffian M(2024)Explainable AI Assisted Decision-Making and Human BehaviourComputing, Internet of Things and Data Analytics10.1007/978-3-031-53717-2_36(376-385)Online publication date: 22-Feb-2024
https://doi.org/10.1007/978-3-031-53717-2_36
Engelmann DFerrando APanisson AAncona DBordini RMascardi V(2022)RV4JaCa – Runtime Verification for Multi-Agent SystemsElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.362.5362(23-36)Online publication date: 20-Jul-2022
https://doi.org/10.4204/EPTCS.362.5
Engelmann DDamasio JPanisson AMascardi VBordini R(2022)Argumentation as a Method for Explainable AI : A Systematic Literature Review2022 17th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI54924.2022.9820411(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.23919/CISTI54924.2022.9820411
Eldrandaly KAbdel-Basset MIbrahim MAbdel-Aziz N(2022)Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directionsEnterprise Information Systems10.1080/17517575.2022.209853717:9Online publication date: 26-Jul-2022
https://doi.org/10.1080/17517575.2022.2098537
Alrabbaa CBorgwardt SHirsch AKnieriemen NKovtunova ARothermel AWiehr F(2022)In the Head of the Beholder: Comparing Different Proof RepresentationsRules and Reasoning10.1007/978-3-031-21541-4_14(211-226)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-21541-4_14
Ferreira CPanisson AEngelmann DVieira RMascardi VBordini R(2022)Explaining Semantic Reasoning Using ArgumentationAdvances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection10.1007/978-3-031-18192-4_13(153-165)Online publication date: 13-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-18192-4_13
Franklin MLagnado D(2022)Human-AI Interaction Paradigm for Evaluating Explainable Artificial IntelligenceHCI International 2022 Posters10.1007/978-3-031-06417-3_54(404-411)Online publication date: 16-Jun-2022
https://doi.org/10.1007/978-3-031-06417-3_54
Panisson AEngelmann DBordini R(2021)Engineering Explainable Agents: An Argumentation-Based ApproachEngineering Multi-Agent Systems10.1007/978-3-030-97457-2_16(273-291)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1007/978-3-030-97457-2_16
Martinho DCarneiro JNeves JNovais PCorchado JMarreiros G(2021)A Reinforcement Learning Approach to Improve User Achievement of Health-Related GoalsProgress in Artificial Intelligence10.1007/978-3-030-86230-5_21(266-277)Online publication date: 7-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86230-5_21

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