activity
20222025
most citedConstitutional AI: Harmlessness from AI Feedback

334 citations · 493 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2025★ 9 cited

Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

Kunal Handa, Alex Tamkin, Miles McCain +12

Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used f…

cs.LG2025★ 2 cited

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions

Taedong Yun, Eric Yang, Mustafa Safdari +13

We present an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle…

cs.CY2024★ 8 cited

Clio: Privacy-Preserving Insights into Real-World AI Use

Alex Tamkin, Miles McCain, Kunal Handa +18

How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users' data, both privacy concerns and practical challe…

cs.CL2023★ 53 cited

The Capacity for Moral Self-Correction in Large Language Models

Deep Ganguli, Amanda Askell, Nicholas Schiefer +46

We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to "morally self-correct" -- to avoid producing harmf…

cs.CL2022★ 52 cited

Discovering Language Model Behaviors with Model-Written Evaluations

Ethan Perez, Sam Ringer, Kamilė Lukošiūtė +60

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (w…

cs.CL2022★ 334 cited

Constitutional AI: Harmlessness from AI Feedback

Yuntao Bai, Saurav Kadavath, Sandipan Kundu +48

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improveme…

cs.HC2022★ 35 cited

Measuring Progress on Scalable Oversight for Large Language Models

Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez +43

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on m…