Quantifying Systemic Risk in Correlated Models: A Methodological Review

Thomas M. Menino Convention & Exhibition Center 

As AI systems are increasingly deployed across organizations and sectors, risk increasingly arises from correlated errors across multiple, ostensibly independent systems. Shared training data, architectures, foundation models, and alignment pipelines can induce synchronized failures, creating accumulation and systemic risks that evade standard single‑model evaluations. This paper surveys methodological approaches for quantifying behavioral similarity among machine learning models, focusing on error correlation as a key indicator of systemic risk. We introduce a risk‑oriented evaluation framework grounded in desirable statistical properties and assess the applicability of existing metrics under realistic auditing constraints. Drawing on recent empirical evidence, we identify common drivers of correlated behavior and examine their implications for downstream deployment risk. We argue that effective AI governance requires shifting from isolated model validation toward portfolio‑level auditing and dependency‑aware risk management aligned with emerging regulatory and assurance frameworks.

Correlated Models

Systemic Risk

Safety‑critical Machine Learning

Auditing

Governance 

Business and Economic Statistics Section