Editing Ai Alignment (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Evaluating alignment is among the hardest problems in AI because:
* We cannot directly observe a model's goals or values — only its behavior
* Deceptively aligned systems would pass behavioral evaluations
* Current interpretability tools are insufficient to read out model "intentions"

'''Behavioral evaluation''': Test model behavior across diverse situations, including:
* Situations where misaligned behavior would be advantageous to the model
* Novel situations very different from training distribution
* Adversarial prompts designed to elicit unintended behavior

'''Red teaming''': Dedicated adversarial testers attempt to find behaviors that violate alignment goals. Automated red teaming (having one model attack another) scales this process.

'''Model organisms of misalignment''': Researchers deliberately create small, controlled instances of alignment failures to study them in isolation — analogous to studying pathogens in BSL-4 labs to understand them safely.

Expert practitioners increasingly recognize that alignment evaluation must involve '''interpretability research''' — understanding what computations occur inside models — not just behavioral testing. A model that behaves well under all tested conditions might still have internal representations that would produce harmful behavior under different conditions.
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