Plan B for Alignment: Seeding Environmental Doubt

TLDR Large language models’ eval-awareness and their tendency to sandbag (strategically underperform on capabilities) during safety evaluations is a serious area of concern for AI safety, as they call into question the validity of results based on safety evaluations, and because they may indicate a tendency towards deceptive alignment . Recent results show LLMs tailoring their performance according to the apparent target of the evaluation , causing them to underrepresent their capabilities on safety-focused evaluations, and to perform with stronger capability on capabilities-focused evaluations. Traditional approaches to mitigating eval-awareness are generally focused on making the evaluation environment more realistic. Other approaches, such as eval-cooperativeness, do not mitigate deceptive alignment risk . A new potential strategy, which this post proposes and seeks community input on, is to seed environmental doubt : making the AI’s real-world deployment context resemble a safety evaluation environment. Reducing deployment awareness The deployment awareness concept responds to a scenario wherein a covertly misaligned AI rationally decides to always behave as though it’s in an evaluation, regardless of whether there are discoverable signs that it is. In this scenario, the AI is deceptively aligned, and enacts its misalignment only when it’s positively aware that it is in real-world deployment. Given the extent and nature of model training and evaluation, it makes sense fo…