An Appraisal-Based Approach to Human-Centred Explanations

TL;DR

This paper introduces an appraisal-based framework inspired by cognitive science to generate human-centered explanations in AI, aiming to improve interpretability in high-stakes domains.

Contribution

It presents a novel explainability approach using the Component Process Model to produce cognitively meaningful, context-sensitive explanations for AI decisions.

Findings

Framework aligns explanations with human cognitive appraisal processes

Provides context-sensitive, meaningful justifications for AI decisions

Bridges cognitive science and explainable AI for better interpretability

Abstract

Explainability remains a critical challenge in artificial intelligence (AI) systems, particularly in high stakes domains such as healthcare, finance, and decision support, where users must understand and trust automated reasoning. Traditional explainability methods such as feature importance and post-hoc justifications often fail to capture the cognitive processes that underlie human decision making, leading to either too technical or insufficiently meaningful explanations. We propose a novel appraisal based framework inspired by the Component Process Model (CPM) for explainability to address this gap. While CPM has traditionally been applied to emotion research, we use its appraisal component as a cognitive model for generating human aligned explanations. By structuring explanations around key appraisal dimensions such as relevance, implications, coping potential, and normative significance our framework provides context sensitive, cognitively meaningful justifications for AI decisions. This work introduces a new paradigm for generating intuitive, human-centred explanations in AI driven systems by bridging cognitive science and explainable AI.