Publikation

Titel:

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

AutorInnen:

Strunk, Jobin
Nissen, Anika
Smolnik, Stefan

Kategorie:

Beiträge in referierten Zeitschriften

erschienen in:

Decision Support Systems. {Link}

Abstract:

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.