Research Article Analysis
Passing the Buck to AI: How Individuals' Decision-Making Patterns Affect Reliance on AI
This research explores how individual decision-making patterns, such as vigilance and buckpassing, influence engagement with and reliance on AI-generated information. Findings from an online experiment with 810 participants highlight that buckpassing correlates with higher AI reliance and less scrutiny of AI explanations, while vigilance leads to more careful evaluation.
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02 March 2026
Published
05 Dec 2025
Accepted
Authors
Katelyn Xiaoying Mei (Information School, University of Washington)
Rock Yuren Pang (Paul G. Allen School of Computer Science, University of Washington)
Alex Lyford (Middlebury College)
Lucy Lu Wang (Information School, University of Washington)
Katharina Reinecke (Paul G. Allen School of Computer Science, University of Washington)
Publication: ACM Transactions on Computer-Human Interaction
DOI: 10.1145/3786326
Executive Summary
Revolutionize Decision-Making with AI-Powered Insights
This study highlights how understanding individual decision-making patterns can significantly enhance Human-AI interaction. By tailoring AI systems to user psychological traits, enterprises can boost decision quality, reduce misinformation risks, and optimize operational efficiency. This is particularly vital for critical decision-making contexts and for users prone to 'buckpassing'.
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Potential Decision Quality Improvement
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Estimated Annual Hours Reclaimed
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Reduction in Misinformation Vulnerability
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ROI Potential from Tailored AI
Deep Analysis & Enterprise Applications
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Research Scope
810 Total Participants in Online ExperimentCore Research Methodology
Questionnaire Completion
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Nutrition Fact/Myth Task
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Optional AI Assistance
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Decision & Explanation Phase
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Post-Task Survey
| Decision Pattern | Impact on AI Reliance & Engagement |
|---|---|
| Vigilance Individuals making decisions after thorough information gathering. |
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| Buckpassing Individuals deferring decisions to others. |
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| Hypervigilance Individuals making rushed and anxious decisions. |
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Demographic Trends
Under 35 Age Group Most Prone to BuckpassingDesigning for Vulnerable Groups: Mitigating Risks for Buckpassers
Individuals with a high buckpassing tendency are more susceptible to misinformation from AI due to lower confidence and higher psychological stress. Designers should use low-effort cognitive forcing functions (e.g., presenting AI suggestions on demand, nudges) and personalized explanation formats (e.g., summary, audio/visual) to encourage active engagement and critical evaluation, rather than blind acceptance. This is crucial for younger demographics and those with lower education levels who show higher buckpassing.
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Estimated Annual Savings
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Total Hours Reclaimed
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Your AI Transformation Roadmap
A typical journey to integrate personalized AI decision support into your enterprise, ensuring maximum impact and adoption.
Phase 1: Discovery & Strategy
Comprehensive assessment of current decision-making processes, identification of key AI integration points, and strategic planning based on your organizational psychology.
Phase 2: Custom AI Development
Tailored AI model development, focusing on cognitive forcing functions and personalized explanation formats, optimized for your user profiles and specific decision challenges.
Phase 3: Pilot & Iteration
Deploy a pilot program within a key department, gather feedback on human-AI interaction patterns, and iterate on AI design for optimal reliance and engagement.
Phase 4: Full-Scale Integration & Training
Seamless integration of AI solutions across the enterprise, coupled with targeted training programs to foster appropriate AI reliance and mitigate risks of misinformation.
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