Calibrated Collective Oversight for Scalable AI Control
A recent publication on arXiv presents Calibrated Collective Oversight (CCO), a strategy designed to ensure human supervision over advanced AI systems that might surpass human abilities. CCO combines various auxiliary scoring functions to create a penalty that assesses deviations from a conservative standard, drawing inspiration from Attainable Utility Preservation. This method promotes collective conservatism: actions incur penalties based on overseer apprehension, allowing for the selection of high-utility actions when deemed acceptable, while overriding them as concerns grow. Utilizing Conformal Decision Theory, CCO adjusts this conservatism in real-time, minimizing the likelihood of adverse outcomes. This approach tackles a critical challenge in AI safety, offering statistical assurances for sequential scenarios. The paper, identified as 2605.28807, is authored by a team of researchers.
Key facts
- Paper introduces Calibrated Collective Oversight (CCO) for scalable oversight of agentic AI
- CCO aggregates diverse auxiliary scoring functions into a penalty measuring deviation from a conservative baseline
- Inspired by Attainable Utility Preservation
- CCO enables collective conservatism: actions penalized proportional to overseer concern
- High-utility actions selected when unobjectionable, overridden when concern accumulates
- CCO calibrates conservatism online using Conformal Decision Theory
- Ensures undesirable outcomes remain unlikely
- Published on arXiv with ID 2605.28807
Entities
Institutions
- arXiv