AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making
Open accessAbstract
In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.
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Users often struggle to balance trust, either overtrusting and overrelying on AI recommendations or undertrusting and disregarding helpful advice.
“Research indicates that users often struggle to find the right balance, either overtrusting and overly relying on AI recommendations or undertrusting and disregarding helpful advice [ 8 , 9 ].”
As clinicians gain experience, they develop a deeper understanding of clinical contexts and decision-making, which may influence trust in and reliance on clinical decision support systems.
“As clinicians gain experience, they develop a deeper understanding of clinical contexts and decision-making processes, which may influence both trust in and reliance on CDSSs [ 8 , 15 - 17 ].”
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“Research ranges from system acceptance and adoption [39,40] to specific investigations of trust in systems [41-44] to the calibration of trust and the appropriateness of reliance [8,9,45-49].”
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“Recent advances in AI emphasise that effective human-AI interaction depends on achieving an appropriate level of reliance, particularly in decision-support contexts (Cabitza et al., 2023; Schemmer et al., 2022).”
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“Automation bias is typically defined as a detrimental over-reliance on DSSs, whereby users defer their judgment to the machine even when independent verification would be warranted [17,18].”
Influential
- Authors
- Cabitza, F., Campagner, A., Angius, R., Natali, C., & Reverberi, C.
- Affiliations
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- University of Padua (IT)
- University of Milano-Bicocca (IT)
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-20
- OpenAlex
- W4366547500
- Cited by
- 51
- References
- 65
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 11 · 2023: 3
- FWCI
- 25.52
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- technological hegemony, AI-supported decision-making, human-AI interaction, automation bias, algorithm appreciation, cognitive biases, measurement framework, clinical decision-making, collaborative decision-making
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