Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems

2025-09-02

AI Summary

AI deployment in high-stakes domains demands explainability to foster trust and accountability, yet prevailing XAI methods often neglect foundational human cognitive mechanisms. This paper introduces the first XAI framework systematically integrating Malle’s five-category model of human explanatory reasoning—namely, knowledge structure, simulation/prediction, covariation, direct recall, and rationalization—by unifying attribution analysis, feature importance, attention visualization, and large language model–generated reasoning chains. The resulting multimodal explanation framework aligns technical outputs with empirically grounded human explanation preferences. Empirical evaluation in real-world credit risk assessment and regulatory compliance tasks demonstrates significant improvements in users’ depth of understanding and perceived trustworthiness of AI decisions. The core contribution lies in pioneering a cognition-informed design paradigm for explainability—shifting XAI from mere *technical interpretability* toward *human comprehensibility*, *acceptability*, and *reliability*.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techniques with Malle's five category model of behavior explanation: Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization. The framework is designed to be applicable across AI assisted decision making systems, with the goal of enhancing transparency, interpretability, and user trust. We demonstrate its practical relevance through real world case studies, including credit risk assessment and regulatory analysis powered by large language models (LLMs). By aligning technical explanations with human cognitive mechanisms, the framework lays the groundwork for more comprehensible, responsible, and ethical AI systems.

Problem

• Bridging AI explainability techniques with human cognitive models
• Enhancing transparency and trust in AI-assisted decision systems
• Applying framework to real-world cases like credit risk assessment

Innovation

• Framework bridges explainability techniques with human cognition model
• Integrates Malle's behavior explanation categories into AI systems
• Demonstrated through real-world LLM applications like risk assessment