Position: Before Scaling Oversight, Measure the Overseer — The Reliability Axis of AI Governing AI
Year: 2026
Type: preprint
Abstract: Position: Before Scaling Oversight, Measure the Overseer — The Reliability Axis of AI Governing AI AI systems increasingly supervise AI systems. The 2026 Singapore Consensus names the gap: "the alignment of overseer AIs is itself unverified." This position paper argues the gap is primarily a reliability problem and proposes the reliability axis of AI-governs-AI as a research direction: (1) four dominant families (constitutional AI, LLM-as-judge, self-critique, scalable oversight) each treat the overseer as an instrument, never as a failure source; (2) the precise fallacy is same-origin governance — shared weights/corpora/alignment mean joint failure cannot fall below the common cause (measured φ = 0.916 for same-model pairs, Zenodo 23696391); (3) the governing discount 1−ρ_cross prices the independence an overseer actually delivers; (4) legitimacy is layered: deterministic green core, heterogeneous-model yellow tier, human red tier; (5) three reporting standards: ρ for every oversight layer, the discount on the invoice, provenance triplicity for heterogeneity claims. A preregistered six-family measurement protocol is registered (Zenodo 23719903). Alternative views are stated and answered.
Author: Zang, Cunxun
Institution: Anhui Institute of Information Technology
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7224576152