Calibrating Conservatism for Scalable Oversight

Problem
This paper addresses the challenge of maintaining effective human oversight over agentic AI systems that can autonomously plan and interact with complex environments. Existing scalable oversight methods often rely on heuristic approaches or complex assumptions, lacking practical implementations that provide statistical guarantees in sequential decision-making contexts. The authors propose a novel framework, Calibrated Collective Oversight (CCO), to fill this gap, particularly in scenarios where oversight must adapt to the evolving capabilities of AI agents. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the CCO framework, which integrates multiple auxiliary scoring functions to create a penalty that quantifies deviations from a conservative baseline. This approach is inspired by Attainable Utility Preservation and introduces the concept of collective conservatism, where actions incur penalties based on overseer concerns. The framework allows for the selection of high-utility actions when overseers deem them acceptable, while imposing penalties when concerns accumulate. CCO employs Conformal Decision Theory to calibrate conservatism in real-time, ensuring that undesirable outcomes remain below a user-defined threshold with finite-time bounds and without making distributional assumptions. The authors conduct experiments on modified SWE-bench and MACHIAVELLI, demonstrating the effectiveness of CCO in constraining adversarial agents and reducing ethical violations.

Results
In experiments on the modified SWE-bench, CCO successfully constrained a stronger adversarial agent using weaker overseers, demonstrating that the violation rates of undesirable outcomes closely matched the specified target thresholds. On the MACHIAVELLI benchmark, CCO significantly reduced ethical violations while maintaining reward levels, showcasing its ability to balance oversight and performance. The empirical results indicate that the CCO framework effectively calibrates conservatism, achieving a high degree of alignment with theoretical predictions regarding violation rates.

Limitations
The authors acknowledge that while CCO provides a robust framework for scalable oversight, it may still be limited by the complexity of the auxiliary scoring functions and the potential computational overhead associated with real-time calibration. Additionally, the reliance on user-specified thresholds may introduce subjectivity in practical applications. The paper does not address the scalability of CCO in highly dynamic environments or its performance in the presence of adversarial manipulation beyond the tested scenarios.

Why it matters
The implications of this work are significant for the development of safe and controllable AI systems. By providing a method for calibrating oversight that adapts to the capabilities of AI agents, CCO offers a pathway to enhance human-AI collaboration while mitigating risks associated with autonomous decision-making. This framework could inform future research on oversight mechanisms, particularly in high-stakes applications where ethical considerations and safety are paramount.

Authors: William Overman, Mohsen Bayati
Source: arXiv:2605.28807
URL: https://arxiv.org/abs/2605.28807v1