title: Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values
    Prioritization with AIRiskDilemmas
summary: 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.
paper_authors._id: 682e09d9aa26daa3c1dfa562
paper_authors.name: Yu Ying Chiu
paper_authors._id: 682e09d9aa26daa3c1dfa563
paper_authors.name: Zhilin Wang
paper_authors._id: 682e09d9aa26daa3c1dfa564
paper_authors.name: Sharan Maiya
paper_authors._id: 682e09d9aa26daa3c1dfa565
paper_authors.name: Yejin Choi
paper_authors._id: 682e09d9aa26daa3c1dfa566
paper_authors.name: Kyle Fish
paper_authors._id: 682e09d9aa26daa3c1dfa567
paper_authors.name: Sydney Levine
paper_authors._id: 682e09d9aa26daa3c1dfa568
paper_authors.name: Evan Hubinger
paper_title: Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values
    Prioritization with AIRiskDilemmas
paper_summary: 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.
paper_ai_summary: Identifying values within AI models using LitmusValues and evaluating them through AIRiskDilemmas and HarmBench can predict both known and unknown risky behaviors.
paper_ai_keywords: LitmusValues AI value classes AIRiskDilemmas value prioritization Power Seeking HarmBench