Transparency vs. Interpretability: A Philosophical Take

 

Philosophy of Explainability (Part 2)

Transparency vs. Interpretability: A Philosophical Take

Introduction: Why This Distinction Matters

In conversations about XAI (Explainable AI), two words get tossed around a lot: transparency and interpretability. They often appear side by side, as if they mean the same thing. But in philosophy—and in practice—they point to very different dimensions of explanation.

Think of it this way:

  • Transparency tells us what is visible.

  • Interpretability tells us what is understandable.

This difference may sound subtle, but it matters enormously. A system can be fully transparent yet remain utterly uninterpretable to its intended audience. Conversely, it can be interpretable without being fully transparent. This article unpacks the philosophical roots of the distinction and explains why striking the right balance is critical for trust in AI.

Defining the Terms

  • Transparency: The extent to which the internal workings of a system are open to inspection. In practice, this could mean open-source code, published model weights, or detailed documentation of data sources.

  • Interpretability: The degree to which humans can make sense of a system’s inputs, outputs, and behavior. It’s about comprehension, not just access.

Philosophically, transparency aligns with ontology (what something is), while interpretability aligns with epistemology (what something means to us).

Case Study 1: Transparency Without Interpretability

Consider GPT-style LLMs (Large Language Models). Many are released with full transparency: millions—even billions—of parameters are published. Anyone can download them.

But what do these parameters mean? To most people (and even to most experts), they are opaque numerical values. Transparency here offers ontological clarity—we know what the system is—but not epistemic clarity. We don’t understand why a specific prompt leads to a specific output.

This is like giving someone the blueprint of a jet engine but not the mechanical training to know how it works. Transparent? Yes. Interpretable? Not really.

Case Study 2: Interpretability Without Transparency

Now consider credit scoring algorithms used by banks. Some are proprietary and not transparent at all. You can’t see the code, the weights, or the training data.

Yet the bank may still provide an interpretable explanation: “Your loan was denied because your income-to-debt ratio exceeded the acceptable threshold.” This doesn’t expose the full internal model, but it does give a meaningful, audience-specific interpretation.

Here, interpretability is delivered without transparency. It may be legally or ethically sufficient, even if technically incomplete.

Philosophical Paradox: The "Transparency Trap"

The distinction raises a paradox: radical transparency can undermine interpretability.

If a hospital released the full decision tree of its AI diagnostic tool, doctors might drown in irrelevant technical detail. Instead of empowering them, this “data dump” obscures what really matters: actionable insights for patient care.

This is what philosophers call the problem of epistemic overload: information without sense-making. Explanations need to be filtered, simplified, and contextualized to serve their purpose.

The Spectrum of Explainability

We can imagine a spectrum between transparency and interpretability:

  1. Opaque / Non-Interpretable: Pure black box (e.g., proprietary credit scoring with no explanation).

  2. Transparent but Non-Interpretable: Full code and weights, but meaningless to end-users (e.g., open LLMs).

  3. Interpretable but Non-Transparent: Simplified narratives without access to underlying details (e.g., bank explanations).

  4. Balanced Transparency + Interpretability: Human-centered explanations backed by traceable evidence (the gold standard for XAI).

Most real-world systems fall somewhere between 2 and 3. Few achieve 4.

Critical Thinking Lens

From a critical thinking perspective, this raises questions of audience-relative adequacy:

  • Clarity: Is the explanation pitched at the right level for its audience?

  • Relevance: Does it provide what the decision-maker actually needs?

  • Sufficiency: Does it offer enough transparency to check accountability without drowning users in details?

These align with the Paul-Elder intellectual standards of clarity, relevance, and sufficiency—reminders that explanation is not just a technical artifact but a cognitive tool.

Case Study 3: EU AI Act and the "Right to Explanation"

The EU AI Act and GDPR regulations emphasize a “right to explanation” for individuals subject to automated decision-making. But regulators face a dilemma:

  • Too much transparency → incomprehensible to ordinary users.

  • Too little transparency → lack of accountability.

The philosophical question becomes: Should the right to explanation be interpreted as a right to transparency or a right to interpretability?

Early interpretations suggest the latter: users deserve explanations they can understand, not just a flood of technical detail.

Practical Implications: Designing Explainability Dashboards

In practice, designers of AI dashboards must choose carefully:

  • For engineers, transparency is key: access to logs, parameters, and error rates.

  • For end-users, interpretability is key: simple visuals, causal stories, and actionable takeaways.

  • For regulators, a hybrid is necessary: enough transparency for audits, enough interpretability for fairness checks.

This is not unlike Plato’s distinction between the visible realm (what is shown) and the intelligible realm (what is understood). Both matter, but for different purposes.

Critical Thinking Prompts

  • Should AI companies prioritize transparency or interpretability when the two conflict?

  • Is interpretability enough without transparency, or does that risk creating “explanation theater” (plausible stories that hide real mechanics)?

  • Would you trust a system more if it was fully transparent but uninterpretable, or partially interpretable but opaque?

Conclusion: Balancing Ontology and Epistemology

Transparency and interpretability are not synonyms; they are complementary but distinct. The philosophy of explainability teaches us that:

  1. Transparency without interpretability overwhelms.

  2. Interpretability without transparency risks manipulation.

  3. The moral task of explainable AI is to strike a balance: revealing enough to hold systems accountable while simplifying enough to make them usable.

In short, transparency is about seeing into the machine, while interpretability is about making sense of it. Neither alone is enough. Together, they form the twin pillars of meaningful explainability.

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