Communicating the Rationale Behind AI Decisions (Explainability)

As Artificial Intelligence (AI) systems become increasingly prevalent in decision-relevant and high-risk domains, the exclusive focus on algorithmic performance is encountering normative and organizational constraints. Predictive accuracy alone is insufficient; there is a growing demand for decisions that are justifiable, verifiable, and accountable. This chapter examines explainability not merely as a technical property of models, but as a communicative and governance-oriented practice that structures interactions among humans, systems, and institutions. The methodology integrates a conceptual analysis of the Explainable AI (XAI) literature with perspectives from human–AI interaction, metacognition research, and governance theory. The findings indicate that explanations primarily influence decision-making by calibrating trust and responsibility through metacognitive processes, rather than by enhancing understanding of model internals. Building on these insights, the chapter introduces the Explainability Assurance Pipeline, a lifecycle-oriented framework for governing explainability.