7. Philosophy of Explainability

 

7. Philosophy of Explainability

Philosophy of Explainability: A Blog Series

Artificial Intelligence (AI) has pushed us to revisit one of philosophy’s oldest questions: What does it mean to explain something? This series explores how the philosophical foundations of explanation, interpretation, and moral responsibility apply to AI systems, particularly LLMs (Large Language Models), neural networks, and emerging explainable AI (XAI) methods.

We’ll ask: What counts as an explanation? Is transparency the same as interpretability? When does explainability become an ethical duty? And, perhaps most provocatively—what can AI reveal about how humans explain and understand?

Article 1: What Counts as an "Explanation" in AI?

An explanation is not just information—it’s a bridge between cause and understanding. In philosophy of science, explanations are often classified as:

In AI, explanation is trickier. A deep learning model might generate outputs without easily interpretable internal steps. So what counts as an explanation here?

  • For developers: a technical breakdown of weights and activations.

  • For policymakers: a clear articulation of biases and risks.

  • For everyday users: an answer to “Why did AI suggest this option for me?”

Philosophy reminds us that an explanation must be audience-relative. What counts as an explanation in AI depends on the expectations and background knowledge of the receiver.

Article 2: Transparency vs. Interpretability: A Philosophical Take

Transparency means access: opening the "black box" so others can see what’s inside.
Interpretability means sense-making: being able to understand what is seen.

A system could be transparent but not interpretable—think of dumping millions of lines of code or raw parameter values on a user. Conversely, it could be interpretable without full transparency—like a simple analogy or a heatmap showing which features influenced a decision.

Philosophically, this raises the distinction between ontological clarity (what the system is) and epistemic clarity (what the system means to us).

  • Transparency → Ontology (revealing structure).

  • Interpretability → Epistemology (revealing understanding).

In practice, good explainability requires a balance—too much transparency can overwhelm, while too little interpretability can mislead.

Article 3: Explainability as a Moral Imperative

Why explain AI at all? Beyond curiosity or technical debugging, explainability is a moral issue.

  • Accountability: If an AI denies a loan or medical treatment, the person affected deserves an explanation.

  • Fairness: Without explanations, hidden biases can go unchallenged.

  • Autonomy: Explanations allow humans to make informed choices instead of blindly trusting automation.

Philosophy of ethics gives us two lenses:

  • Deontological duty (Kantian ethics): We have a duty to respect persons as rational agents, which requires offering reasons.

  • Consequentialist ethics (Utilitarianism): Explanations maximize well-being by preventing harm and building trust.

Thus, explainability is not just a feature—it’s a moral safeguard against abuse of power in an algorithmic society.

Article 4: What AI Can Teach Us About Human Cognition

Here’s the twist: studying AI explainability also shines light on how we explain.

Humans, too, often give post hoc rationalizations rather than genuine causal accounts. Cognitive science shows that when asked “Why did you do that?”, people often construct stories after the fact. AI mirrors this through techniques like saliency maps or LIME (Local Interpretable Model-Agnostic Explanations)—tools that provide approximations, not full truths.

This leads to two provocative insights:

  1. Human explanations are not absolute—they’re adaptive, simplified, and tailored to context, just like AI’s.

  2. AI exposes the limits of human cognition—forcing us to rethink whether “perfect” explanations are possible, or even desirable.

Philosophy suggests that the value of explanation lies not in total transparency, but in making sense together. AI can thus serve as a mirror for our own epistemic humility—the recognition that understanding is always partial.

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