Understanding XAI Through the Philosopher's Lens: A Historical Perspective

XAI lacks a unified theoretical foundation, but philosophy of science offers valuable lessons—the paper reveals that both fields have independently evolved from rigid logical-deductive models toward more flexible statistical and probabilistic approaches to explanation, suggesting XAI could benefit from adopting philosophical frameworks already developed over centuries.

The shift from deterministic to probabilistic thinking is fundamental to understanding modern AI explanations—just as philosophy of science moved away from the idea that explanations must follow strict logical rules, XAI must embrace that explanations in machine learning systems are inherently probabilistic rather than absolute, which has implications for how we communicate AI decisions to stakeholders.

"Understanding" and "explanation" are not the same thing—the paper identifies that both philosophy and AI have grappled with the distinction that an explanation can be technically correct without actually helping someone understand why something happened, a critical insight for designing AI systems that genuinely communicate their reasoning to users.

Context matters more than technologists often acknowledge—the paper highlights that pragmatic factors (who is asking, what do they need to know, what is the use case) shape what counts as a "good" explanation in both philosophy and AI, meaning one-size-fits-all XAI methods are unlikely to work across different applications and audiences.

Responsible AI development should draw on centuries of philosophical debate—by grounding XAI in established epistemological principles rather than treating it as purely a technical problem, organizations can build more robust and trustworthy AI systems with clearer expectations about what explanations can and cannot deliver.