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 e