为事后解释方法正名：无需完全透明也能产生科学洞见
In Defence of Post-hoc Explainability
- 用中介性理解与有限真理性构建哲学框架
- 实证验证下，事后解释可生成新假设并提升现象认知
- 适合关注模型可解释性与科学发现的科研人员
本文捍卫事后解释方法在机器学习科学知识生产中的正当性。针对其可靠性与认识论地位的批评，我们提出基于中介性理解与有限真理性的哲学框架。论证表明，只要承认解释的近似性质并经过严格的实证验证，即便不完全透明，也能通过结构化地解读模型行为获得科学洞见。通过对近期生物医学机器学习应用的分析，证明当事后解释方法被恰当融入科学实践时，可生成新的研究假说，并推动对现象本质的理解。
原文摘要 · Abstract (English)
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.
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