Explainability and justification of automatic-decision making: A conceptual framework and a practical application

2026-03-02

AI Summary

This study addresses the persistent gap in current automated decision-making systems, which often provide explanations without effectively convincing recipients of the legitimacy of their decisions. Distinguishing between “explanation”—clarifying how a decision was generated—and “justification,” which offers normatively acceptable reasons, this work introduces Habermas’s theory of communicative action and Perelman’s new rhetoric into explainable artificial intelligence (XAI) research for the first time. It proposes a recipient-centered framework for analyzing justification, grounded in these theoretical foundations. The framework’s theoretical and practical utility is demonstrated through a case study of university admissions in France, revealing how different forms of explanation variably support justificatory claims. This approach offers a novel paradigm for developing trustworthy automated decision systems that prioritize not only transparency but also normative acceptability from the perspective of affected stakeholders.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Knowledge Representation and Reasoning: Argumentation

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Explainability of algorithmic decision-making systems is both a regulatory objective and an area of intense research. The article argues that a crucial condition for the acceptability of algorithmic decision-making systems is that decisions must be justified in the eyes of their recipients. We make a clear distinction between explanation and justification. Explanations describe how a decision was made, while justifications give reasons that aim to make the decision acceptable. We propose a conceptual framework of explanations and justifications, based on Habermas's theory of communicative action and Perelman's New Rhetoric theory of law. This framework helps to analyze how different forms of explanation can support or fail to support justification. We illustrate our approach with a case study on university admissions in France.

Problem

• explainability
• justification
• algorithmic decision-making
• acceptability
• explanation

Innovation

• explainability
• justification
• algorithmic decision-making
• conceptual framework
• communicative action