解释性AI的本质是因果建模，没有因果就无法真正解释。
Position: Explainable AI is Causality in Disguise
- 将解释性问题转化为因果探究，用因果模型作为解释基础
- 证明因果模型是实现可解释性的必要且充分条件
- 适合关注可信AI、公平性与模型透明度的研究者
解释性AI（XAI）方法的激增导致领域碎片化，缺乏统一标准，冲突指标、失败的合理性检验和对鲁棒性与公平性的争论持续存在。尽管普遍认为‘正确解释’缺乏真实基准，本文主张真正的基准并非缺失，而是以复杂难测的因果模型形式存在。通过将数据、模型或决策的解释性问题重构为因果问题，我们证明因果模型是实现解释性的必要且充分条件。若无因果基础，解释性将失去根基。因此，本文呼吁社区聚焦于先进概念与因果发现技术，以摆脱当前的不确定性困境。
原文摘要 · Abstract (English)
The demand for Explainable AI (XAI) has triggered an explosion of methods, producing a landscape so fragmented that we now rely on surveys of surveys. Yet, fundamental challenges persist: conflicting metrics, failed sanity checks, and unresolved debates over robustness and fairness. The only consensus on how to achieve explainability is a lack of one. This has led many to point to the absence of a ground truth for defining ``the'' correct explanation as the main culprit. This position paper posits that the persistent discord in XAI arises not from an absent ground truth but from a ground truth that exists, albeit as an elusive and challenging target: the causal model that governs the relevant system. By reframing XAI queries about data, models, or decisions as causal inquiries, we prove the necessity and sufficiency of causal models for XAI. We contend that without this causal grounding, XAI remains unmoored. Ultimately, we encourage the community to converge around advanced concept and causal discovery to escape this entrenched uncertainty.
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