Nurturing Knowledge: A Virtue Epistemology Approach to Explainable AI

AI technologies, particularly deep neural networks and machine learning models, have become increasingly integrated into knowledge production across diverse scientic domains, raising critical concerns about explainability and interpretability. Different disciplines and contexts require fundamentally different types of explanations, making universal approaches to explainable AI inadequate. Virtue epistemology offers a promising framework for addressing these challenges by focusing on how AI systems cultivate or undermine epistemic virtues within specic knowledge communities. Rather than seeking explanations in abstract terms, virtue epistemology emphasizes epistemic abilities and character traits as they manifest within particular epistemic cultures. Case studies from social science, neuroscience, medicine, and the humanities reveal that meaningful progress in explainable AI requires aligning computational reasoning with the cultivation of epistemic virtues and the mitigation of epistemic vices that characterize each scientic community’s specialized knowledge practices and norms.