Towards AI epidemiology: a measurement standardisation framework for prospective risk detection

2025-12-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new framework, "AI epidemiology," is proposed for prospective risk detection in deployed AI systems. This framework standardizes expert-AI interactions into structured, comparable fields, crucially operating without access to model internals. The paper, submitted on 15 Dec 2025 and last revised 4 Jun 2026, defines the framework's semantic and statistical scope and outlines a protocol for future empirical testing. It posits three claims: large language models can reliably assess expert-AI interaction alignment, these alignment scores can serve as immediate governance signals and monitoring tools, and aggregate scores could enable "AI epidemiology" to detect risk by studying associations with downstream outcomes in regulated settings. The current work addresses the reliability claim and specifies protocols for the other two, detailing a statistical approach involving paired bootstrap inference, DeLong's test for paired AUCs, a 0.05 non-inferiority margin, and Holm-Bonferroni correction.

Key takeaway

For AI governance professionals evaluating deployed systems, this framework offers a novel approach to prospective risk detection. You should consider implementing standardized expert-AI interaction assessments to generate alignment scores. These scores provide immediate signals and a basis for monitoring risk patterns across diverse models and domains. This enables an "AI epidemiology" without needing internal model access, shifting your focus to external, correlated variables for risk identification.

Key insights

A framework enables "AI epidemiology" by standardizing expert-AI interactions for prospective risk detection without model internals.

Principles

• Standardized measurement enables population-level AI risk detection.
• LLMs can reliably assess expert-AI interaction alignment.
• External alignment scores provide governance signals.

Method

The framework defines a grammar and uses paired bootstrap inference, DeLong's test for paired AUCs, a 0.05 non-inferiority margin, and Holm-Bonferroni correction for empirical evaluation.

In practice

• Standardize expert-AI interactions for risk monitoring.
• Use LLMs for reliable alignment assessments.
• Monitor aggregate alignment scores for risk patterns.

Topics

• AI Epidemiology
• Risk Detection
• AI Governance
• Measurement Standardization
• Large Language Models
• Statistical Protocols

Best for: Research Scientist, AI Scientist, MLOps Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.