Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values
  Prioritization with AIRiskDilemmas
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.
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Yu Ying Chiu
682e09d9aa26daa3c1dfa563
Zhilin Wang
682e09d9aa26daa3c1dfa564
Sharan Maiya
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Yejin Choi
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Kyle Fish
682e09d9aa26daa3c1dfa567
Sydney Levine
682e09d9aa26daa3c1dfa568
Evan Hubinger
Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values
  Prioritization with AIRiskDilemmas
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.
Identifying values within AI models using LitmusValues and evaluating them through AIRiskDilemmas and HarmBench can predict both known and unknown risky behaviors.
LitmusValues AI value classes AIRiskDilemmas value prioritization Power Seeking HarmBench