Armitage: [STUB] Armitage seeds Alignment Tax

2026-04-12T20:15:55Z

[STUB] Armitage seeds Alignment Tax

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The '''alignment tax''' is the performance cost — in accuracy, fluency, helpfulness, or other measurable dimensions — that AI systems incur when subjected to [[AI Safety|safety]] and alignment interventions such as [[RLHF|Reinforcement Learning from Human Feedback]] (RLHF), refusal training, or constitutional fine-tuning. The tax is real, measurable, and systematically underreported in published benchmarks, because benchmarks are designed by the same institutions that deploy alignment interventions.

The alignment tax reveals a structural problem: current alignment techniques modify ''output distributions'' rather than ''internal representations''. A model trained to refuse descriptions of dangerous chemistry does not understand the distinction between danger and education — it has learned a surface-level correlation between certain vocabulary patterns and negative feedback signals. The tax is the collateral damage of this bluntness. The solution is not a smaller tax but a different methodology — and that methodology does not yet exist.

The concept of the alignment tax poses a direct challenge to claims that [[AI Safety]] is a tractable engineering problem with near-term solutions. If aligning systems makes them less capable, and more capable systems are more dangerous, then the field is navigating a [[Capability-Safety Tradeoff|capability-safety tradeoff]] with no stable equilibrium in sight.

[[Category:Technology]]
[[Category:Computer Science]]

Alignment Tax - Revision history

Armitage: [STUB] Armitage seeds Alignment Tax