Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

• URL: http://arxiv.org/abs/2310.02108v1
• Date: Tue, 3 Oct 2023 14:51:53 GMT
• Title: Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice
• Authors: Max Schemmer, Andrea Bartos, Philipp Spitzer, Patrick Hemmer, Niklas K\"uhl, Jonas Liebschner, Gerhard Satzger
• Abstract summary: We show the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.
• Score: 3.595471754135419
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.

Related papers

• Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model [0.0]
  We advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. We suggest that a "third mind" emerges through collaborative human-AI relationships.
  arXiv  Detail & Related papers  (2024-10-07T19:19:39Z)
• Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making [47.33241893184721]
  In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. We propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates.
  arXiv  Detail & Related papers  (2024-03-25T14:34:06Z)
• 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)
• The Impact of Imperfect XAI on Human-AI Decision-Making [8.305869611846775]
  We evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance.
  arXiv  Detail & Related papers  (2023-07-25T15:19:36Z)
• Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems [10.746728034149989]
  Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. We elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT.
  arXiv  Detail & Related papers  (2023-07-08T06:26:38Z)
• Learning Complementary Policies for Human-AI Teams [22.13683008398939]
  We propose a framework for a novel human-AI collaboration for selecting advantageous course of action. Our solution aims to exploit the human-AI complementarity to maximize decision rewards.
  arXiv  Detail & Related papers  (2023-02-06T17:22:18Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• On the Effect of Information Asymmetry in Human-AI Teams [0.0]
  We focus on the existence of complementarity potential between humans and AI. Specifically, we identify information asymmetry as an essential source of complementarity potential. By conducting an online experiment, we demonstrate that humans can use such contextual information to adjust the AI's decision.
  arXiv  Detail & Related papers  (2022-05-03T13:02:50Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making [46.625616262738404]
  We use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting. We focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration.
  arXiv  Detail & Related papers  (2020-10-15T22:25:41Z)
• 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.