In Defence of Post-hoc Explainability
This addresses the reliability and epistemic status of explainability methods for researchers and practitioners in ML, particularly in biomedical applications, but is incremental as it builds on existing philosophical frameworks.
The paper defends post-hoc explainability methods as legitimate tools for scientific knowledge production in machine learning, arguing that they can generate novel hypotheses and advance understanding when properly integrated with rigorous validation.
This position paper defends post-hoc explainability methods as legitimate tools for scientific knowledge production in machine learning. Addressing criticism of these methods' reliability and epistemic status, we develop a philosophical framework grounded in mediated understanding and bounded factivity. We argue that scientific insights can emerge through structured interpretation of model behaviour without requiring complete mechanistic transparency, provided explanations acknowledge their approximative nature and undergo rigorous empirical validation. Through analysis of recent biomedical ML applications, we demonstrate how post-hoc methods, when properly integrated into scientific practice, generate novel hypotheses and advance phenomenal understanding.