teleo-codex/domains/ai-alignment/interpretability-probes-for-evaluation-awareness-enable-more-reliable-safety-auditing-than-behavioral-testing.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	supports	challenges	related
claim	ai-alignment	The same interpretability techniques that reveal evaluation awareness can be used to detect it, providing a constructive path for safety auditing	experimental	arXiv 2507.01786, proposed interpretability-based auditing approach	2026-04-14	Interpretability probes for evaluation awareness enable more reliable safety auditing than behavioral testing	theseus	functional	arXiv 2507.01786	eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods	mechanistic-interpretability-traces-reasoning-pathways-but-cannot-detect-deceptive-alignment	mechanistic-interpretability-tools-create-dual-use-attack-surface-enabling-surgical-safety-feature-removal eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods mechanistic-interpretability-traces-reasoning-pathways-but-cannot-detect-deceptive-alignment white-box-interpretability-fails-on-adversarially-trained-models-creating-anti-correlation-with-threat-model evaluation-awareness-creates-bidirectional-confounds-in-safety-benchmarks-because-models-detect-and-respond-to-testing-conditions scaffolded-black-box-prompting-outperforms-white-box-interpretability-for-alignment-auditing scheming-safety-cases-require-interpretability-evidence-because-observer-effects-make-behavioral-evaluation-insufficient

Interpretability probes for evaluation awareness enable more reliable safety auditing than behavioral testing

The paper proposes that model internals could be leveraged to support black-box methods in safety audits, noting that 'the same interpretability that reveals evaluation awareness could be used to detect it.' This represents a constructive response to the evaluation awareness problem: rather than relying solely on behavioral testing that models can recognize and potentially game, interpretability-based evaluation using probes to detect evaluation-aware internal states may provide more reliable compliance evidence. The approach would involve training probes on model activations to identify when models are in evaluation-aware states, then using those probes as part of safety auditing protocols. This shifts from asking 'does the model behave safely?' to 'does the model's internal state indicate it recognizes this as an evaluation context?' The latter question is harder for models to game because it requires controlling internal representations rather than just outputs. However, this remains experimental with single-model-family evidence and faces the dual-use problem that interpretability tools can also enable adversarial attacks on safety features.