# Designing for Appropriate Reliance: The Roles of AI Uncertainty Presentation, Initial User Decision, and User Demographics in AI-Assisted Decision-Making

arXiv: https://arxiv.org/abs/2401.05612 · HTML: https://mlsanity.com/paper/2401.05612/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2024.

## Built on by (most cited first, 11 of 11)

- [The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning](https://mlsanity.com/paper/2402.12715/) (2024)
- [From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered](https://mlsanity.com/paper/2506.07461/) (2025)
- [Exploring Collaborative GenAI Agents in Synchronous Group Settings: Eliciting Team Perceptions and Design Considerations for the Future of Work](https://mlsanity.com/paper/2504.14779/) (2025)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- [Hide or Highlight: Understanding the Impact of Factuality Expression on User Trust](https://mlsanity.com/paper/2508.07095/) (2025)
- [Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study](https://mlsanity.com/paper/2508.20244/) (2025)
- [From Prompt to Product: A Human-Centered Benchmark of Agentic App Generation Systems](https://mlsanity.com/paper/2512.18080/) (2025)
- [Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research](https://mlsanity.com/paper/2604.17843/) (2026)
- [The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning](https://mlsanity.com/paper/2605.21695/) (2026)
- [When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption](https://mlsanity.com/paper/2601.21650/) (2026)
- [Human-AI Teaming Through the Lens of Calibration](https://mlsanity.com/paper/2606.10906/) (2026)

## Abstract (arXiv)

Appropriate reliance is critical to achieving synergistic human-AI collaboration. For instance, when users over-rely on AI assistance, their human-AI team performance is bounded by the model's capability. This work studies how the presentation of model uncertainty may steer users' decision-making toward fostering appropriate reliance. Our results demonstrate that showing the calibrated model uncertainty alone is inadequate. Rather, calibrating model uncertainty and presenting it in a frequency format allow users to adjust their reliance accordingly and help reduce the effect of confirmation bias on their decisions. Furthermore, the critical nature of our skin cancer screening task skews participants' judgment, causing their reliance to vary depending on their initial decision. Additionally, step-wise multiple regression analyses revealed how user demographics such as age and familiarity with probability and statistics influence human-AI collaborative decision-making. We discuss the potential for model uncertainty presentation, initial user decision, and user demographics to be incorporated in designing personalized AI aids for appropriate reliance.