# Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction

arXiv: https://arxiv.org/abs/2604.07535 · HTML: https://mlsanity.com/paper/2604.07535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- ["Are You Really Sure?" Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2403.09552/) (2024)
- [As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making](https://mlsanity.com/paper/2501.12868/) (2025)
- [From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI](https://mlsanity.com/paper/2002.10433/) (2020)
- [On the Utility of Domain Modeling Assistance with Large Language Models](https://mlsanity.com/paper/2410.12577/) (2024)
- [A Reference Architecture of Reinforcement Learning Frameworks](https://mlsanity.com/paper/2603.06413/) (2026)
- [Authorship Drift: How Self-Efficacy and Trust Evolve During LLM-Assisted Writing](https://mlsanity.com/paper/2602.05819/) (2026)

## Abstract (arXiv)

Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when the human user is prompted by an AI agent, e.g., for advice or guidance. In this paper, we show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user's self-confidence and self-efficacy. In the long run, the loss of confidence may lead to performance loss, suboptimal decisions, human errors, and ultimately, unsustainable AI systems. Our evidence comes from an experiment with 30 human participants. Our results indicate that users may feel more confident in their work when they are eased into the human-AI setup rather than exposed to it without preparation. We elaborate on the implications of this finding for software engineers and decision-makers.