Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making

• URL: http://arxiv.org/abs/2410.16560v1
• Date: Mon, 21 Oct 2024 22:39:52 GMT
• Title: Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making
• Authors: Nikita Haduong, Noah A. Smith,
• Abstract summary: We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
• Score: 57.53469908423318
• License:
• Abstract: AI systems are used in many domains to assist with decision making, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms. Investigation into why the performance potential is not realized has revealed many factors, including (mis)trust in the AI system and mental models of AI capabilities on subjective tasks. Performance pressure is known to influence human decision making behavior, yet how it interacts with human-AI decision making is understudied. In this work, we show the effects of performance pressure on AI advice reliance when laypeople (Amazon Mechanical Turk crowdworkers) complete a common AI-assisted task (fake review detection) and thus have inherently low performance pressure. We manipulate performance pressure by leveraging people's loss aversion towards potential monetary gains when completing a task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation. Furthermore, when the AI system gives incorrect advice, people correctly discount the poor advice more often when the stakes are higher than when they are lower. We conclude by discussing the implications of how performance pressure influences AI-assisted decision making and encourage future research to incorporate performance pressure analysis.

Related papers

• To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems [11.690126756498223]
  Vision for optimal human-AI collaboration requires 'appropriate reliance' of humans on AI systems. In practice, the performance disparity of machine learning models on out-of-distribution data makes dataset-specific performance feedback unreliable.
  arXiv  Detail & Related papers  (2024-09-22T09:43:27Z)
• Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making [48.179458030691286]
  We examine three AI roles: Recommender, Analyzer, and Devil's Advocate. Our results show each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience. These insights offer valuable implications for designing AI assistants with adaptive functional roles according to different situations.
  arXiv  Detail & Related papers  (2024-03-04T07:32:28Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems [13.484359389266864]
  This paper addresses whether the Dunning-Kruger Effect (DKE) can hinder appropriate reliance on AI systems. DKE is a metacognitive bias due to which less-competent individuals overestimate their own skill and performance. We found that participants who overestimate their performance tend to exhibit under-reliance on AI systems.
  arXiv  Detail & Related papers  (2023-01-25T14:26:10Z)
• Improving Human-AI Collaboration With Descriptions of AI Behavior [14.904401331154062]
  People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions.
  arXiv  Detail & Related papers  (2023-01-06T00:33:08Z)
• Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making [10.890854857970488]
  Many factors can impact success of Human-AI teams, including a user's domain expertise, mental models of an AI system, trust in recommendations, and more. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Our results show that while recommendations from an AI-Assistant can aid user decision making, factors such as users' baseline performance relative to the AI and complementary tuning of AI error types significantly impact overall team performance.
  arXiv  Detail & Related papers  (2022-08-16T21:39:58Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
  We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
  arXiv  Detail & Related papers  (2020-04-27T19:06:28Z)
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
  We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
  arXiv  Detail & Related papers  (2020-01-07T15:33:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.