# Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure arXiv: https://arxiv.org/abs/2306.07458 ยท HTML: https://mlsanity.com/paper/2306.07458/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 68 references that are in the map. - First posted on arXiv 12 Jun 2023. ## Builds on (most cited first, 12 of 12) - [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020) - [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020) - [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018) - [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018) - [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020) - [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021) - [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023) - [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020) - [In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making](https://mlsanity.com/paper/2305.07722/) (2023) - [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023) - [Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative](https://mlsanity.com/paper/2103.04725/) (2021) - [Learning When to Advise Human Decision Makers](https://mlsanity.com/paper/2209.13578/) (2022) ## Built on by (most cited first, 17 of 17) - [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024) - [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024) - [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024) - [From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis](https://mlsanity.com/paper/2502.11919/) (2025) - [When Should We Orchestrate Multiple Agents?](https://mlsanity.com/paper/2503.13577/) (2025) - [Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistance](https://mlsanity.com/paper/2502.13321/) (2025) - [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026) - [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025) - [A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning](https://mlsanity.com/paper/2406.00116/) (2024) - [Revisiting Rogers' Paradox in the Context of Human-AI Interaction](https://mlsanity.com/paper/2501.10476/) (2025) - [Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study](https://mlsanity.com/paper/2512.09088/) (2025) - [A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations](https://mlsanity.com/paper/2507.10585/) (2025) - [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) - [SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches](https://mlsanity.com/paper/2606.17646/) (2026) - [How AI Assistance Affects Human Skill Development: A Study of Learning with Logic Puzzles](https://mlsanity.com/paper/2608.23543/) (2026) - [No Pixel Left Behind: Filling Gaps in Anime Colorization](https://mlsanity.com/paper/2609.00800/) (2026) ## Abstract (arXiv) In settings where users both need high accuracy and are time-pressured, such as doctors working in emergency rooms, we want to provide AI assistance that both increases decision accuracy and reduces decision-making time. Current literature focusses on how users interact with AI assistance when there is no time pressure, finding that different AI assistances have different benefits: some can reduce time taken while increasing overreliance on AI, while others do the opposite. The precise benefit can depend on both the user and task. In time-pressured scenarios, adapting when we show AI assistance is especially important: relying on the AI assistance can save time, and can therefore be beneficial when the AI is likely to be right. We would ideally adapt what AI assistance we show depending on various properties (of the task and of the user) in order to best trade off accuracy and time. We introduce a study where users have to answer a series of logic puzzles. We find that time pressure affects how users use different AI assistances, making some assistances more beneficial than others when compared to no-time-pressure settings. We also find that a user's overreliance rate is a key predictor of their behaviour: overreliers and not-overreliers use different AI assistance types differently. We find marginal correlations between a user's overreliance rate (which is related to the user's trust in AI recommendations) and their personality traits (Big Five Personality traits). Overall, our work suggests that AI assistances have different accuracy-time tradeoffs when people are under time pressure compared to no time pressure, and we explore how we might adapt AI assistances in this setting.