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What is the Best Explainable Artificial Intelligence for Enhancing Self-Regulation Behavior in Healthcare Management? Evidence from A Randomized Field Experiment
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- 발행기관
- 한국경영정보학회 바로가기
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- 간행물
- 한국경영정보학회 정기 학술대회 바로가기
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- 통권
- 2023년도 한국경영정보학회 추계 학술대회 (2023.11)바로가기
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- 페이지
- pp.140-148
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A444613
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원문정보
초록
- 영어
- Despite the widespread use of artificial intelligence (AI) in mobile healthcare apps, the need for more transparency in AI algorithms hinders their effectiveness by preventing users from understanding the reasons behind AI-based information provision. To address this challenge, various types of explainable AI (XAI) are adopted to offer transparent explanations of AI. Despite significant debates surrounding AI intervention, limited research has been devoted to whether and how various XAI types affect user behavior differently. In this study, we conducted a randomized field experiment to investigate the effectiveness of three XAI algorithms: 1) feature importance, 2) feature attribution, and 3) counterfactual explanation in promoting users' health behavior. Drawing on the self-regulated learning theory, we expect that XAI focusing on counterfactual explanation increases strategic planning and outcome expectancy, resulting in better self-regulation behavior. Our findings indicate that counterfactual explanation significantly improves users' action planning behavior, leading to a 16.5% increase in workout duration and a 3.49% increase in health records compared to the control group. Our results are salient for users with a high level of AI susceptibility due to age, goal weight loss, and AI outcome. Our finding sheds light on the potential of algorithmic explanations to improve the effectiveness of AI interventions in the healthcare industry, with practical implications for designing more transparent and user-friendly healthcare apps.
목차
Abstract
Introduction
Related Work
Randomized Field Experiment
Institutional Background
AI-based Model Development
Experimental Design and Process
Data and Empirical Approach
Results
Discussion
References
키워드
explainable artificial intelligence counterfactual explanation healthcare self-regulated learning weight management저자
간행물 정보
발행기관
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- 발행기관명
- 한국경영정보학회 [The Korea Society of Management information Systems]
- 설립연도
- 1989
- 분야
- 사회과학>경영학
- 소개
- 이 학회는 경영정보학의 연구 및 교류를 촉진하고 학문의 발전과 응용에 공헌함을 목적으로 합니다.
간행물
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- 간행물명
- 한국경영정보학회 정기 학술대회 [KMIS Conference]
- 간기
- 반년간
- 수록기간
- 1990~2026
- 십진분류
- KDC 325 DDC 658
이 권호 내 다른 논문 / 한국경영정보학회 정기 학술대회 2023년도 한국경영정보학회 추계 학술대회
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