📣 Open Call for Posters! Submit your work to the poster session at the CHAI 2026 Workshop. Link below! ⏱️ Deadline: March 26, 2026 at 11:59p.m. PST. 🗓 June 4–7, 2026 at the Asilomar Conference Grounds in Pacific Grove, CA.

Active Teacher Selection for Reward Learning: now published in TMLR! Most RLHF systems assume feedback comes from one canonical teacher — but annotators can disagree over 30% of the time. So who should the agent ask for feedback? Paper: arxiv.org/abs/2310.15288v3

How do knowledge and meaning change in the age of AI, and what can we learn from silence and art? We explored these and many other deep questions in this amazing event at Pomona last month. youtube.com/watch?v=dG9JuK3S…

My internship work at @CHAI_Berkeley (@UCBerkeley) was accepted to @aistats_conf! We study how an agent can act cautiously even without a mentor/oracle: when should it act, and when should it abstain to avoid catastrophic failure? 📄Paper: arxiv.org/abs/2510.14884 🧵

How to elicit truth from models that may be mistaken❌ or deceptive😈? In our @CHAI_Berkeley paper @iclr_conf, we reward each model by how much its answer helps predict the others'. With weak supervision from a 0.14B LM, it enables anti-deception training on a 8B LM and overwhelmingly outperforms LLM-as-a-Judge. This technique, peer prediction, is adapted from the mechanism design literature, where it's known to be incentive-compatible, i.e., incentivizes honesty. The intuition is that, predicting mistakes/lies when you know the correct solution is relatively easy, while the opposite is asymmetrically hard. We are able to further show that, with a large and diverse pool of models, peer prediction incentivizes honesty even when the supervisor doesn't know the models' prior beliefs and motivations.

What if an AI could learn to hide its thoughts? We show that LLMs can learn a general skill to evade activation monitors, with 0-shot transfer to unseen deception/harmfulness monitors from the literature. We call these "Neural Chameleons". A thread on our new paper. 🦎🧵

Our NeurIPS 2025 paper extends adversarial learning (adversarial examples, self-play, etc.) beyond zero-sum games by solving "self-sabotage". 🧵👇

The Global Call for AI Red Lines is live!! More than 200 former heads of state, Nobel laureates, and other respected thinkers and leaders, and 70 organizations are together calling for “do not cross” limits re: AI’s most severe #risks

Who should apply? Current undergrads, Master’s, and PhD students and researchers, researchers in CS or adjacent fields, professional software or ML engineers.... The list goes on! If you're highly motivated to make progress on AI safety, consider applying.

Our interns: • Contribute to research with the potential for paper authorship • Build a pathway into AI safety work • Work alongside curious and ethically minded researchers

*New AI Alignment Paper* 🚨 Goal misgeneralization occurs when AI agents learn the wrong reward function, instead of the human's intended goal. 😇 We show that training with a minimax regret objective provably mitigates it, promoting safer and better-aligned RL policies!