The Black Box Dilemma - Azetta.ai Blog

a cathedral of unknowns

there's a quiet crisis unfolding in artificial intelligence. not a crisis of capability—our systems grow more powerful by the month. not a crisis of access—models are deployed at unprecedented scale. the crisis is deeper, more fundamental: we don't understand what we've built.

modern AI systems are cathedrals of unknowns. billions of parameters, trained on trillions of tokens, optimized by algorithms we can barely analyze. they diagnose diseases, generate legal contracts, write code, approve loan applications. they make decisions that alter lives. and when we ask them why—why this diagnosis, why this rejection, why this answer—they cannot tell us.

not because they're hiding something. because they genuinely don't know. and neither do we.

"what i cannot create, i do not understand." — richard feynman

feynman's famous maxim cuts both ways. we have created these systems—but have we understood them? or have we merely assembled them from parts we don't fully grasp, following procedures that happen to work?

the price of opacity

the black box problem isn't abstract philosophy. it manifests in concrete failures, every day, at scale.

consider the doctor who receives an AI recommendation for cancer treatment. the model was trained on millions of cases, its accuracy is statistically impressive. but when the patient asks—as patients always ask—why this treatment?—the doctor can only shrug. the model said so. trust the math.

or consider the applicant denied a mortgage. somewhere in a forest of decision trees, a boundary was crossed. which feature? which threshold? which historical bias encoded in the training data? the bank cannot say. the model cannot explain. and the applicant has no recourse against a decision they cannot understand.

the cascade of consequences:

accountability vacuum — when systems fail, who is responsible? the engineer? the company? the model itself?
debugging impossibility — when a black box fails, we cannot pinpoint why. we retrain and hope.
bias amplification — hidden biases become invisible, perpetuating inequities at machine speed.
trust erosion — users are asked to trust what they cannot understand, creating a dangerous precedent.

these aren't edge cases. they're the everyday reality of deploying systems we cannot interpret. and as AI systems become more powerful, the stakes only grow.

the explainability illusion

the industry's response to opacity has been post-hoc explainability: techniques like LIME, SHAP, and attention visualization that attempt to explain what a model did after the fact.

these tools have their place. but they suffer from a fundamental limitation: they explain a different model.

LIME, for instance, fits a simple linear model around a prediction to approximate the decision boundary locally. but this approximation is not the model's actual reasoning—it's a simplified story we tell ourselves about what the model might have been thinking. SHAP assigns credit to features based on game-theoretic principles, but these attributions are computed from counterfactuals the model never actually evaluated.

attention weights are perhaps the most seductive illusion. we visualize them as if they reveal what the model "looks at." but attention is not explanation—it's a learned weighting scheme that happens to correlate with interpretable patterns sometimes, and completely fails to sometimes.

the core problem: post-hoc explanations are approximations of approximations. they don't reveal the model's actual reasoning—they construct plausible narratives that may or may not reflect reality. it's like asking a fortune teller to explain quantum mechanics.

the architecture trap

here's the uncomfortable truth: opacity isn't a bug. it's a feature—an emergent property of the architectures we've chosen.

neural networks work by learning distributed representations. concepts aren't stored in individual neurons—they're smeared across millions of weights in patterns we cannot isolate. a single weight participates in countless computations. a single computation involves countless weights. the entanglement is total.

this distributed nature is precisely what makes neural networks powerful. it enables generalization, robustness to noise, graceful degradation. but it also ensures that no single component has interpretable meaning. the power and the opacity are two sides of the same coin.

we've built systems that succeed because they're uninterpretable.

"the more powerful our models become, the less we understand them. this is not a paradox—it's the signature of a fundamentally wrong approach."

a different foundation

the solution isn't better post-hoc explanations. it's a different architecture from the start.

what if transparency wasn't an afterthought but a constraint? what if we demanded that every computation have semantic meaning—that we could trace every decision back to first principles?

this isn't as radical as it sounds. physics achieved it centuries ago. we can predict eclipses millennia in advance not because we have enormous datasets of past eclipses, but because we understand the laws governing planetary motion. the equations are interpretable. the predictions are explanatory. knowledge and capability are unified.

in beyond the brain, we'll explore why physics—not biology—should be our model for intelligence. but the seed of that argument is already here: if you don't understand your system's computations, you don't understand your system. period.

the path to glass boxes

building transparent AI requires us to reconsider our foundational assumptions.

first: optimize for interpretability alongside accuracy. these aren't competing objectives—they're complementary constraints that force more elegant solutions. the history of science shows that the most powerful theories are also the most explainable.

second: ground representations in meaningful structures. embeddings shouldn't be arbitrary points in high-dimensional space. they should reflect the actual geometry of concepts—distances that mean something, directions that point somewhere. we explore this in the meaning of information.

third: choose metrics that encode reality. how we measure similarity between observations encodes our entire theory of what "same" and "different" mean. get the metric wrong, and even a perfectly transparent system will give you the wrong answers. this is the subject of the meaning of non-linearity.

the thesis of this series: the black box dilemma is not inevitable. it's a choice—a consequence of architectures we selected and assumptions we made. by rebuilding AI from first principles, with transparency as a core constraint, we can create systems that are both powerful and understood.

what comes next

this is the first article in a series exploring the foundations of interpretable intelligence. we'll journey from the philosophy of information to the mathematics of similarity, from the limits of biological inspiration to the principles of physics that might replace it.

the goal isn't just to understand the black box problem—it's to transcend it. to build a new kind of AI where knowledge and capability are unified, where power and transparency are not at odds.

the black box dilemma doesn't have to be a dilemma. it's just where we are. not where we have to stay.