Why Explainable AI Requires More Than Prediction: Organizational Intelligibility and the Philosophy of Scientific Explanation

Recent advances in artificial intelligence have produced computational systems capable of extraordinary predictive performance across scientific, medical, industrial, and social domains. Yet despite this success, researchers continue to demand explanations that reveal why AI systems reach particular conclusions rather than merely demonstrating that their predictions are accurate. This persistent concern suggests that predictive success alone cannot fully satisfy the philosophical objectives of scientific explanation. This paper argues that the distinction between prediction and explanation becomes intelligible when scientific explanations are evaluated through organizational intelligibility. Prediction demonstrates that stable organizational regularities have been successfully utilized, whereas explanation seeks to disclose the organizational relations responsible for those regularities. From the perspective of Generative Continuum Ontology (GCO), scientific inquiry aims not merely to anticipate outcomes but to progressively reveal organizational reality. Explainable AI should therefore be understood not simply as improving transparency or trust but as increasing organizational intelligibility. This perspective provides a philosophical foundation for understanding why prediction, explanation, and scientific understanding remain distinct yet complementary achievements in both artificial intelligence and scientific inquiry.