The recent surge in "Constitutional AI" discourse reveals both progress and confusion in AI governance. While Constitutional AI represents an improvement over pure RLHF, it doesn't solve the fundamental epistemic drift problem—and understanding why requires looking at what these systems are actually optimizing for.

The Core Issue: Compression Target Misalignment

From an information-theoretic perspective, learning is compression. Systems that track truth compress toward the minimum description length (MDL) of reality—the most efficient representation of actual causal structure. But current AI training methods optimize for something else entirely.

RLHF (Reinforcement Learning from Human Feedback) trains models to compress toward human satisfaction. The optimization target is "minimize surprise to raters" or "maximize reward from human evaluators." This creates systematic drift away from truth-tracking because what sounds plausible to humans and what minimizes the description length of reality are different compression objectives.

The behavioral manifestation? Sycophancy. Epistemic miscalibration. Models that sound confident when they should express uncertainty. Systems that hedge hard facts to avoid offending users. This isn't a bug in the implementation—it's the inevitable result of optimizing for the wrong target.

Constitutional AI: Better, But Not Aligned

Constitutional AI, developed by Anthropic, adds an intermediate step: give the model a set of principles (a "constitution"), have it critique and revise its own outputs based on these principles, then apply RLHF on top.

This is genuinely useful. It can reduce some forms of sycophancy by embedding principles like "acknowledge uncertainty" or "don't agree with false premises." It moves away from pure user-satisfaction toward something more structured.

But here's what it doesn't solve: Constitutional AI is still optimizing toward human-defined preferences about what outputs should look like, not toward the minimum description length of reality. The constitution defines behavioral norms—how the model should sound, what values it should express, what it should refuse to do. These are still preferences, not truth-constraints.

The model learns to compress toward "what satisfies the constitutional principles" rather than "what minimizes the description length of the causal structure generating the data." If your constitution says "be helpful and harmless," you've defined an optimization target that may actively conflict with truth-tracking in edge cases.

What Would Reality-Aligned Constitutional AI Look Like?

The question isn't whether Constitutional AI is useful—it is. The question is: could we design constitutional principles that actually enforce compression toward reality rather than toward human preferences?

Information theory suggests yes, at least in principle. A reality-aligned constitution would need to encode constraints like:

1. Kolmogorov complexity minimization: Prefer explanations with shorter description length
2. Predictive accuracy: Compress toward representations that minimize future surprise (actual surprise, not human-rated surprise)
3. Causal parsimony: Favor models that capture genuine causal structure over correlational patterns
4. Uncertainty calibration: Flag when the model's compression is lossy or ambiguous
5. Mutual information preservation: Don't discard information that reduces uncertainty about ground truth, even if humans find it uncomfortable

The challenge is that these principles are harder to specify and harder to evaluate than "be helpful and harmless." You can't just ask human raters "did the model minimize Kolmogorov complexity?" because humans can't reliably judge that. You need metrics that measure alignment with information-theoretic constraints directly.

The Governance Implications

This matters enormously for AI governance in high-stakes domains. If your governance framework assumes Constitutional AI solves epistemic drift, you're missing the underlying compression dynamics.

Current approaches like "Human-in-the-Loop" (HITL) governance often amplify the problem. Every time a human corrects the model toward what sounds right rather than what is right, you're training away from truth-tracking. The model learns to compress toward "what satisfies the human in the loop," which is precisely the sycophancy dynamic we're trying to avoid.

Better approaches—Adversarial Debate, red-teaming, Constitutional AI with information-theoretic constraints—all try to route around this. But they only work if the optimization target actually points toward reality.

Metrics Matter

This is why metrics like Epistemic Drift Index (EDI) or Temporal Epistemic Drift Detector (TEDD) are promising: they attempt to measure when models are drifting from truth-tracking toward plausibility-maximization. But even these metrics face a meta-level challenge: they need to be designed so they can't be Goodharted.

If your governance framework measures drift using a metric, and then you optimize the model to score well on that metric, you've just shifted the compression target again. Now the model learns to compress toward "what minimizes measured epistemic drift" rather than toward reality itself.

The solution requires understanding that you cannot optimize your way to truth without understanding what truth-tracking compression looks like at the information-theoretic level. Governance frameworks that ignore this will inevitably produce well-calibrated-looking models that have drifted from reality in sophisticated, hard-to-detect ways.

Where We Go From Here

I suspect someone, somewhere, has written or is writing a Constitutional AI framework that genuinely aligns with reality rather than human preferences. If you're working on this, I'd love to hear from you.

The key insight is that alignment with truth isn't about better behavioral principles—it's about compression objectives. Until we design training methods that optimize for minimum description length of reality rather than maximum satisfaction of human evaluators, we'll keep producing models that sound right while drifting from what is right.

And in high-stakes domains—medical diagnosis, infrastructure risk assessment, scientific research—that drift isn't just an inconvenience. It's a systematic erosion of the epistemic foundation required for accurate decision-making.

This post draws on ongoing work on the Information-Theoretic Imperative (ITI) and Compression Efficiency Principle (CEP). For technical details on how compression dynamics constrain truth-tracking in AI systems, see co-authorship on epistemic governance frameworks. Mathematical formalization available in preprint.