CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations

• URL: http://arxiv.org/abs/2604.27354v1
• Date: Thu, 30 Apr 2026 03:12:11 GMT
• Title: CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
• Abstract summary: XAI aims to improve user understanding and decisions when using AI models.<n>Recent user evaluations reveal that this goal remains elusive.<n>We examined various reasoning strategies for different XAI methods in the decision task of anticipating AI decisions.
• Score: 8.572512533736312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations reveal that this goal remains elusive. Understanding human cognition can help explain why users struggle to effectively use AI explanations. Focusing on reasoning on structured (tabular) data, we examined various reasoning strategies for different XAI methods (none, feature importance, feature attribution) in the decision task of anticipating AI decisions (i.e., forward simulation). We i) elicited reasoning strategies from a formative user study, and ii) collected decisions from a summative user study. Using cognitive modeling, we implemented the processes underlying each reasoning strategy and evaluated their alignment with human decision-making. We found that our models better fit human decisions than baseline machine learning proxies, providing insights into which reasoning strategies are (in)effective. We then demonstrate how the fitted model can be used to form hypotheses and investigate research questions that are costly to study with real human participants. This work contributes to debugging human understanding of XAI, informing the future development of more usable and interpretable AI explanations.

Related papers

• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary [19.884253335528317]
  Recent advances in AI models have increased the integration of AI-based decision aids into the human decision making process. To fully unlock the potential of AI-assisted decision making, researchers have computationally modeled how humans incorporate AI recommendations into their final decisions. Providing AI explanations to human decision makers to help them rely on AI recommendations more appropriately has become a common practice.
  arXiv  Detail & Related papers  (2024-11-02T18:33:28Z)
• How Performance Pressure Influences AI-Assisted Decision Making [52.997197698288936]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Study on the Helpfulness of Explainable Artificial Intelligence [0.0]
  Legal, business, and ethical requirements motivate using effective XAI. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task. In other words, we address the helpfulness of XAI for human decision-making.
  arXiv  Detail & Related papers  (2024-10-14T14:03:52Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• 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.<n>We propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making.<n>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)
• Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making [1.0878040851638]
  Machine learning models are error-prone and cannot be used autonomously. Explainable Artificial Intelligence (XAI) aids end-user understanding of the model. This paper surveyed the recent empirical studies on XAI's impact on human-AI decision-making.
  arXiv  Detail & Related papers  (2023-12-11T22:35:21Z)
• Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations [44.01143305912054]
  We study how decision-makers' intuition affects their use of AI predictions and explanations. Our results identify three types of intuition involved in reasoning about AI predictions and explanations. We use these pathways to explain why feature-based explanations did not improve participants' decision outcomes and increased their overreliance on AI.
  arXiv  Detail & Related papers  (2023-01-18T01:33:50Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions [4.169915659794568]
  People need more awareness of how AI works and its outcomes to build a relationship with that system. In decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system.
  arXiv  Detail & Related papers  (2021-02-10T14:28:34Z)
• 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.