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

Current explainability methods often fail in practice: Despite widespread recognition that AI transparency is essential for trust and accountability—particularly in high-stakes domains like credit decisions and regulatory compliance—existing explanation techniques frequently fall short of their intended purpose.

Human cognition should guide AI explanation design: The framework bridges technical AI explanations with how humans naturally understand behavior through five cognitive categories (Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization), suggesting that better AI explanations require understanding human psychology, not just technical sophistication.

One-size-fits-all explanations are insufficient: By proposing a general-purpose framework applicable across different AI systems—from credit risk assessment to LLM-powered regulatory analysis—the paper implies that effective explainability requires tailored approaches rather than standardized technical outputs.

Responsible AI requires alignment between systems and users: The emphasis on enhancing "transparency, interpretability, and user trust" suggests that truly ethical AI systems must be designed with end-users' cognitive needs in mind, not just regulatory compliance or developer convenience.

Real-world testing validates theoretical approaches: The inclusion of practical case studies in credit assessment and regulatory analysis demonstrates that cognitive frameworks for explainability can move beyond theory to improve actual decision-making systems where people's lives and livelihoods are affected.