Center for Human-Compatible AI

CHAI’s mission is to develop the conceptual and technical wherewithal to reorient the general thrust of AI research towards provably beneficial systems.

 

 
 

 
 
 
 
 
 
 
 RvS: What is Essential for Offline RL via Supervised Learning? 

 Scott Emmons, PhD student, was an author on “RvS: What is Essential for Offline RL via Supervised Learning?”

 
 
 
 
 
 
 
 
 
 A Practical Definition of Political Neutrality for AI 

 NEW: Our current research project to build political neutrality evaluations .

 
 
 
 
 
 
 
 
 
 
 
 Computational Frameworks for Human Care 

 Brian Christian, CHAI Affiliate, has published an article titled “ Computational Frameworks for Human Care ” in the most recent issue of Daedalus, the journal of the American Academy of Arts and Sciences. In it, Christian traces how AI alignment has progressed from simple reward mechanisms toward care-like relationships, revealing both the potential and limitations of machine caregiving while deepening our understanding of human care itself. The issue is titled “The Social Science of Caregiving” and was co-edited by CHAI Affiliate Alison Gopnik.

 
 
 
 
 
 
 
 
 
 Learning to Coordinate with Experts 

 Khanh Nguyen, Benjamin Plaut, Tu Trinh, and Mohamad Danesh introduce a fundamental coordination problem called Learning to Yield and Request Control (YRC), where the objective is to learn a strategy that determines when to act autonomously and when to seek expert assistance. They build an open-source benchmark featuring diverse domains, propose a novel validation approach, and investigate the performance of various learning methods across diverse environments, yielding insights that can guide future research.