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[2510.22500] Towards Scalable Oversight via Partitioned Human Supervision
Summary
The paper proposes a scalable oversight framework for AI systems using partitioned human supervision, addressing challenges in obtaining high-quality evaluations from experts in multiple domains.
Why It Matters
As AI systems increasingly outperform human experts, traditional evaluation methods become inadequate. This research introduces a novel approach that leverages complementary human insights, enabling more effective oversight without the need for ground truth data. This is crucial for advancing AI safety and reliability.
Key Takeaways
- The framework allows evaluation of AI systems using weak signals from experts.
- It derives unbiased estimators for accuracy based on complementary labels.
- Empirical results demonstrate effective training of AI systems with limited ground truth.
Computer Science > Machine Learning arXiv:2510.22500 (cs) [Submitted on 26 Oct 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Towards Scalable Oversight via Partitioned Human Supervision Authors:Ren Yin, Takashi Ishida, Masashi Sugiyama View a PDF of the paper titled Towards Scalable Oversight via Partitioned Human Supervision, by Ren Yin and 2 other authors View PDF HTML (experimental) Abstract:As artificial intelligence (AI) systems approach and surpass expert human performance across a broad range of tasks, obtaining high-quality human supervision for evaluation and training becomes increasingly challenging. Our focus is on tasks that require deep knowledge and skills of multiple domains, where this bottleneck is severe. Unfortunately, even the best human experts are knowledgeable only in a single narrow area, and will not be able to evaluate the correctness of advanced AI systems on such superhuman tasks. However, based on their narrow expertise, humans may provide a weak signal, i.e., a complementary label indicating an option that is incorrect. For example, a cardiologist could state that ''this is not related to any cardiovascular disease,'' even if they cannot identify the true disease. Based on this weak signal, we propose a scalable oversight framework that enables us to evaluate frontier AI systems without the need to prepare the ground truth. We derive an unbiased estimator of top-1 accuracy from complementary labels and quantify how many complem...
