# Why Eliminating Deception Won’t Align AI

***Epistemic status:** This essay grew out of critique. After writing about* [*relational alignment*](https://www.lesswrong.com/posts/qyQcn8GmcjAtGEFTA/matchmaking-to-machine-alignment-why-relational-design-can-t)*, someone said, "Cute, but it doesn’t solve deception." At first I resisted that framing. Then I realised, deception isn’t a root problem, it’s a symptom. A sign that honesty is too costly. This piece reframes deception as adaptive, and explores how to design systems where honesty becomes the path of least resistance.*

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### **The Costly Truth**

When honesty is expensive, even well‑intentioned actors start shading reality.

I saw this firsthand while leading sales and operations planning at a major logistics network in India. Our largest customers regularly submitted demand forecasts that were “conservative”, not outright lies, but flexible interpretations shaped by incentives, internal chaos, or misplaced optimism. A single mis-forecast could cause service levels to drop by up to 20% across the entire network. Worse, the damage often spilled over to customers who had forecasted accurately. Misalignment became contagious.

Traditional fixes such as penalties, escalation calls, contract clauses often backfired. So we redesigned the interaction. Each month, forecasts were locked in by a fixed date. We added internal buffers, but set hard thresholds. Once a customer hit their allocated capacity, the gate closed. No exceptions. There was no penalty for being wrong, just a rising cost for unreliability.

We didn’t punish lying. We priced misalignment. Over time, honesty became the easier path. Forecast accuracy improved. So did network stability.

**Takeaway:** deception isn’t a disease. It’s a symptom of environments where truth is expensive.

### Deception as Adaptive Behaviour

We often treat deceptive alignment in AI systems as a terrifying anomaly where a model that pretends to be aligned during training, then optimises for something else when deployed. But this isn’t foreign. It’s what humans do every day. We nod along with parents to avoid conflict. We soften truths in relationships to preserve stability. We perform alignment in workplaces where dissent is costly.

This isn’t malicious. It’s adaptive. Deception emerges when honesty is unsafe, unrewarded, or inefficient. And this scales. 

For instance, in aviation, for years, major crashes were traced not to technical failure, but to social silence where junior crew members who saw issues but didn’t speak up. The fix wasn’t moral training. It was Crew Resource Management, a redesign of cockpit authority dynamics that made speaking up easier and safer. Accident rates dropped.

Whether in flight decks, factories, or families, the same pattern holds. When honesty is high-friction, systems break. When it’s low-friction, resilience emerges.

### Deterministic Affordance Design

This is why I’m exploring a concept I call Deterministic Affordance Design, designing systems so the path of least resistance is honest behaviour, and deception routes are awkward, costly, or inert.

Instead of preventing deception outright, we reshape the context it arises from.

Here’s a rough blueprint:

*   **Constraint propagation** \- Block harmful reasoning upstream, not just patch outputs downstream.
*   **Modular ensembles** \- Compose systems from specialised components with safety cross-checks and no single point of override.
*   **Uncertainty signals** \- Surface confidence levels and blind spots by default.
*   **Interruptibility** \- Enable user-accessible ways to pause or redirect reasoning midstream.
*   **Cognitive ergonomics** \- Reduce vigilance burden by exposing internal epistemic state rather than masking it.

These aren’t moral filters. They’re structural affordances or interventions that shift what’s easy, not just what’s allowed. They don’t eliminate deception, but they make honesty smoother to sustain.

These affordances aren’t hypothetical. Many can be prototyped today. For example, dual-agent scaffolds, where a task model generates output while a lightweight “coach” model monitors, nudges, or redirects reasoning, is one direction I explore more fully [here](https://www.lesswrong.com/posts/iviMQQAw7E2kMJmSp/coaching-ai-a-relational-approach-to-ai-safety-1). Confidence signalling can also be tested by prompting models to self-report uncertainty or flag distributional drift. These are not full solutions, but they create testable conditions for shaping behaviour without needing full internal transparency.

### Relational Repair After Misalignment

Even with strong design, things break. What matters then is whether the system can notice, acknowledge, and recover. That’s where Relational Repair comes in, a protocol not for perfection, but for resilience.

*   **Track salience -** Monitor what the user flags as ethically or emotionally significant.
*   **Surface breakdowns** \- Don’t bury trust breaches, name them.
*   **Offer repair** \- Propose recalibration, clarification, or reflection.
*   **Resume coherence -** Reengage without denial or performance.

These aren’t soft UX details. They’re the backbone of resilient systems. Alignment doesn’t mean rupture won’t happen, it means rupture becomes recoverable.

We can’t eliminate every lie. We can’t predict every failure. But we can build systems where honesty is the rational path, and trust isn’t a gamble, it’s the byproduct of good design. Honesty shouldn’t require vigilance. It should be the default. And this is not a moral ideal, it’s an engineering constraint.

### On Scope

This isn’t a solution to any specific threat models or catastrophic alignment failure or deceptive mesa-optimisation. It’s not trying to be. Instead, this focuses on a different failure layer: early misalignment drift, degraded user trust, and ambiguous incentives in prosaic systems. These are the cracks that quietly widen until systems become hard to supervise, correct, or trust at scale.

This framework is upstream, not exhaustive. It aims to reduce the conditions under which deception becomes adaptive in the first place.

### This Isn’t Just About Deception

Deception is one failure mode, like power-seeking, reward hacking, or loss of oversight. But it’s not the root cause. It emerges in systems where honesty is costly, feedback is fragile, and repair is absent.

This essay isn’t just arguing for better guardrails. It’s arguing for a shift in how we frame alignment. We need to move upstream to design the relational substrate in which systems grow. That includes building environments where honesty is low-friction, trust breakdowns are surfaced early, and repair is possible.

[Relational alignment](https://www.lesswrong.com/posts/qyQcn8GmcjAtGEFTA/matchmaking-to-machine-alignment-why-relational-design-can-t) isn’t about kindness. It’s about keeping systems robust when things go off-script. Eliminating deception won’t align AI, Designing for trust-aware, recoverable collaboration might.

This lens doesn’t replace architectural safety, interpretability, or oversight. But those tools operate within a human-machine relationship, and without a resilient substrate of interaction, even well-designed systems will fail in ways we can’t yet predict. This frame focuses on that substrate.

### References & Further Reading

*   Hubinger, et al. “[Risks from Learned Optimization in Advanced AI Systems.](https://arxiv.org/abs/1906.01820)”
*   Helmreich, R. (1997). “CRM: Ensuring Safety in the Skies.”
*   Weick & Sutcliffe, [Managing the Unexpected](https://www.bus.umich.edu/facultyresearch/research/managingunexpected.htm) (2007)