# How Performance Pressure Influences AI-Assisted Decision Making

arXiv: https://arxiv.org/abs/2410.16560 · HTML: https://mlsanity.com/paper/2410.16560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 81 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [A Decision Theoretic Framework for Measuring AI Reliance](https://mlsanity.com/paper/2401.15356/) (2024)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Investigating Mysteries of CoT-Augmented Distillation](https://mlsanity.com/paper/2406.14511/) (2024)
- [Trust and Reliance in XAI -- Distinguishing Between Attitudinal and Behavioral Measures](https://mlsanity.com/paper/2203.12318/) (2022)
- [GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence](https://mlsanity.com/paper/2402.12566/) (2024)
- [Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Study](https://mlsanity.com/paper/2101.02555/) (2021)
- [Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making](https://mlsanity.com/paper/2209.03821/) (2022)
- [USB: A Unified Summarization Benchmark Across Tasks and Domains](https://mlsanity.com/paper/2305.14296/) (2023)
- [LIMEADE: From AI Explanations to Advice Taking](https://mlsanity.com/paper/2003.04315/) (2020)
- [Risks and NLP Design: A Case Study on Procedural Document QA](https://mlsanity.com/paper/2408.11860/) (2024)

## Built on by (most cited first, 1 of 1)

- [Offloading Score: Measuring AI Reliance Through Counterfactual Workflows](https://mlsanity.com/paper/2605.29392/) (2026)

## Abstract (arXiv)

Many domains now employ AI-based decision-making aids, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms due to factors such as (mis)trust in the AI system and beliefs about AI being incapable of completing subjective tasks. One potential tool for influencing human decision making is performance pressure, which hasn't been much studied in interaction with human-AI decision making. In this work, we examine how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior. Using an inherently low-stakes task (spam review classification), we demonstrate effective and simple methods to apply pressure and influence human AI advice-taking behavior by manipulating financial incentives and imposing time limits. Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior. We conclude by discussing the implications of these interactions, strategies to effectively use pressure, and encourage future research to incorporate pressure analysis.