Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values
  Prioritization with AIRiskDilemmas
Authors: name: Yu Ying Chiu, name: Zhilin Wang, name: Sharan Maiya, name: Yejin Choi, name: Kyle Fish, name: Sydney Levine, name: Evan Hubinger

Detecting AI risks becomes more challenging as stronger models emerge and
find novel methods such as Alignment Faking to circumvent these detection
attempts. Inspired by how risky behaviors in humans (i.e., illegal activities
that may hurt others) are sometimes guided by strongly-held values, we believe
that identifying values within AI models can be an early warning system for
AI's risky behaviors. We create LitmusValues, an evaluation pipeline to reveal
AI models' priorities on a range of AI value classes. Then, we collect
AIRiskDilemmas, a diverse collection of dilemmas that pit values against one
another in scenarios relevant to AI safety risks such as Power Seeking. By
measuring an AI model's value prioritization using its aggregate choices, we
obtain a self-consistent set of predicted value priorities that uncover
potential risks. We show that values in LitmusValues (including seemingly
innocuous ones like Care) can predict for both seen risky behaviors in
AIRiskDilemmas and unseen risky behaviors in HarmBench.