All risks ain't the same – A risk facets perspective on AI-based decision support systems

社会心理的 透视图(图形) 风险感知 心理学 风险评估 背景(考古学) 风险分析(工程) 桥接(联网) 服务(商务) 风险管理 IT风险 业务 社会心理学 风险治理 风险管理工具 决策支持系统 知识管理 金融服务 社会支持 应用心理学 财务风险 社会环境 服务提供商 产品(数学) 精算学 面(心理学) IT风险管理
作者
Jobin Strunk,Anika Nissen,Stefan Smolnik
出处
期刊:Decision Support Systems [Elsevier BV]
卷期号:199: 114557-114557 被引量:2
标识
DOI:10.1016/j.dss.2025.114557
摘要

Artificial intelligence-based decision support systems (AI-DSSs) transform decision-making across diverse contexts including healthcare, finance, and personalized product and service recommendations. Each context exposes users to a dominant risk facet, such as physical risk when using a health-related AI-DSS, financial risk when using a robo-advisor, or psychosocial risk when interacting with an AI-DSS integrated in a social app. Utilizing risk theory, we systematically analyze how different risk facets and severities influence trust in and advice taking from AI-DSSs. We conduct a between-subjects online experiment with 958 participants who interact with AI-DSSs, covering three major risk facets and two risk severities for each. Our results reveal that risk facets and severities partially jointly influence advice taking. Additionally, while advice-taking in physical risk scenarios remains relatively stable across severity levels, financial and psychosocial contexts show significantly greater sensitivity to changes in risk severity. This highlights an interaction effect, demonstrating that the impact of risk severity on advice-taking is partially influenced by the risk facet. Furthermore, we found that trust mediates the effect of risk facet and risk severities on advice taking. Our insights enhance the theoretical understanding of the interplay between risk, trust, and advice taking in human-AI-DSS interaction. We contribute by bridging critical gaps in current literature, enriching the discourse on AI-DSS trust and advice taking in risk-laden environments. This helps developers of AI-DSSs understand the influence of risk facets related to their service and adapt their digital offerings accordingly. • Between-subjects online experiment with participants interacting with AI-DSSs. • Risk facets and risk levels influence trust in and reliance on AI-DSSs. • Trust is a strong predictor for reliance across different AI-DSSs. • The classic hierarchy of needs shifts when interacting with AI-DSSs.
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