The Value of Information in Human-AI Decision-making

• URL: http://arxiv.org/abs/2502.06152v3
• Date: Tue, 15 Apr 2025 19:26:06 GMT
• Title: The Value of Information in Human-AI Decision-making
• Authors: Ziyang Guo, Yifan Wu, Jason Hartline, Jessica Hullman,
• Abstract summary: We provide a decision-theoretic framework for characterizing the value of information.<n>We demonstrate the use of the framework for model selection, empirical evaluation of human-AI performance, and explanation design.<n>We propose a novel information-based explanation technique that adapts SHAP, a saliency-based explanation, to explain information value in decision making.
• Score: 23.353778024330165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple agents -- including humans and AI models -- are often paired on decision tasks with the expectation of achieving complementary performance, where the combined performance of both agents outperforms either one alone. However, knowing how to improve the performance of a human-AI team is often difficult without knowing more about what particular information and strategies each agent employs. We provide a decision-theoretic framework for characterizing the value of information -- and consequently, opportunities for agents to better exploit available information -- in AI-assisted decision workflows. We demonstrate the use of the framework for model selection, empirical evaluation of human-AI performance, and explanation design. We propose a novel information-based explanation technique that adapts SHAP, a saliency-based explanation, to explain information value in decision making.

Related papers

• Agentic Knowledgeable Self-awareness [79.25908923383776]
  KnowSelf is a data-centric approach that applies agents with knowledgeable self-awareness like humans. Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge.
  arXiv  Detail & Related papers  (2025-04-04T16:03:38Z)
• The Effect of Explainable AI-based Decision Support on Human Task Performance: A Meta-Analysis [0.0]
  We conduct a meta-analysis to explore how XAI affects human performance in classification tasks. Our results show an improvement in task performance through XAI-based decision support. The analysis reveals that the studies' risk of bias moderates the effect of explanations in AI, while the explanation type appears to play only a negligible role.
  arXiv  Detail & Related papers  (2025-03-22T10:38:43Z)
• Unexploited Information Value in Human-AI Collaboration [23.353778024330165]
  How to improve performance of a human-AI team is often not clear without knowing what particular information and strategies each agent employs.<n>We propose a model based in statistical decision theory to analyze human-AI collaboration.
  arXiv  Detail & Related papers  (2024-11-03T01:34:45Z)
• ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments [14.105935964906976]
  This work considers a problem setup where an intelligent agent provides advice to a human decision-maker. We develop an approach named ADESSE to generate explanations about the adviser agent to improve human trust and decision-making.
  arXiv  Detail & Related papers  (2024-05-31T08:59:20Z)
• 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)
• Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information [7.475784495279183]
  One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI.<n>We conduct a behavioral study to explore the effects of providing contextual information to support this delegation decision.<n>Our findings reveal that access to contextual information significantly improves human-AI team performance in delegation settings.
  arXiv  Detail & Related papers  (2024-01-09T18:59:47Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations [53.01494092422942]
  The number and complexity of personalised and user-centred approaches to XAI have rapidly grown in recent years. It emerged that user-centred approaches to XAI positively affect the interaction between users and systems. We propose an assessment task to objectively and quantitatively measure the goodness of XAI systems.
  arXiv  Detail & Related papers  (2023-12-07T15:49:39Z)
• Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
  We propose a method for a collaborative system that remains safe by having a human making decisions. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
  arXiv  Detail & Related papers  (2023-11-13T16:00:16Z)
• From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions [1.1510009152620668]
  We argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time. First, we introduce a highly established computational framework that assumes decisions to emerge from the noisy accumulation of evidence. Next, we discuss to what extent current approaches in multi-agent AI do or do not incorporate process data and models of decision making.
  arXiv  Detail & Related papers  (2023-08-29T11:27:22Z)
• Assisting Human Decisions in Document Matching [52.79491990823573]
  We devise a proxy matching task that allows us to evaluate which kinds of assistive information improve decision makers' performance. We find that providing black-box model explanations reduces users' accuracy on the matching task. On the other hand, custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance.
  arXiv  Detail & Related papers  (2023-02-16T17:45:20Z)
• Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations [62.997667081978825]
  Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
  arXiv  Detail & Related papers  (2022-04-25T02:47:25Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

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.