Towards Auditing AI Systems in the Wild

2026-06-15

AI Summary

Current evaluations of AI systems predominantly rely on static benchmarks, which fail to capture behavioral risks in dynamic real-world environments. This work formalizes AI auditing as an uncertainty-aware, dynamic constraint monitoring problem across the system’s entire lifecycle, targeting critical attributes such as fairness and safety while integrating sociotechnical norms with statistical risk control. By developing a theoretical framework and supporting infrastructure for continuous auditing, the study advances AI governance beyond one-off testing toward ongoing, reliable, and accountable oversight mechanisms.

Abstract

AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users and infrastructure. Traditional machine learning evaluation focuses on benchmarks and operates within sandboxed environments, providing only a limited view of the true system behavior in the wild. We argue for the development of principled auditing frameworks that monitor deployed AI systems throughout their lifecycle. We further propose framing auditing as a statistical problem of monitoring constraint violations under uncertainty, where desired properties (e.g., fairness and safety) are treated as risk-controlled constraints that must be continuously evaluated as systems evolve through iterative feedback. This perspective highlights the need for uncertainty-aware monitoring methods, socio-technical specifications of audit criteria, and auditing infrastructures that enable ongoing oversight of AI systems in the wild.

Problem

• AI auditing
• real-world deployment
• constraint violations
• uncertainty-aware monitoring
• lifecycle oversight

Innovation

• AI auditing
• constraint violation monitoring
• uncertainty-aware evaluation
• lifecycle monitoring
• risk-controlled constraints