A Peircean framework for explainable and human-centred artificial intelligence

Abstract

This article develops a philosophical-conceptual framework for rethinking Explainable Artificial Intelligence (XAI) and Human-Centred Artificial Intelligence (HCAI) through the pragmatic semiotics of Charles Sanders Peirce. Rather than treating explainability solely as algorithmic transparency, the article conceptualizes it as a socially situated epistemic process through which AI-generated outputs are interpreted, evaluated, contested, and revised within human practices of inquiry. Drawing on Peirce’s concepts of semiosis, abduction, inquiry, fallibilism, pragmaticism, and the community of inquiry, the framework distinguishes between computational processes performed by AI systems and the human and institutional practices through which their outputs acquire epistemic and practical significance. AI outputs may function as signs and may generate hypotheses that are functionally analogous to abductive possibilities; however, interpretation, evidential assessment, justification, ethical judgment, and responsibility remain situated within human communities of inquiry. The framework further translates Peircean concepts into practical principles for XAI, including contextual interpretation, uncertainty communication, contestability, human review, stakeholder participation, evidence-based revision, and continuous evaluation. A worked example involving generative AI illustrates how an AI-generated explanation can move through interpretation, questioning, evidence checking, contestation, and revision rather than being accepted as a final epistemic product. The article therefore positions Peircean pragmatism not as a replacement for technical XAI methods but as a philosophical and normative framework that complements them by clarifying the epistemological conditions of meaningful explanation, trustworthy human oversight, and responsible AI governance.