Position: Explainable AI is Causality in Disguise

TL;DR

This paper argues that explainable AI fundamentally relies on understanding the underlying causal models of systems, suggesting causality as the core for achieving true explainability.

Contribution

It reframes XAI as a causal inquiry, demonstrating that causal models are essential and sufficient for meaningful explanations in AI systems.

Findings

Causal models are necessary for true explainability.

Reframing XAI as causal inquiry resolves many debates.

Encourages focus on causal discovery for progress.

Abstract

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.