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Evidence record 927 · automatically gathered

Towards Auditing AI Systems in the Wild

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

Record details

Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Transparency · Environment
Retrieved: 14 July 2026

source-onlyevidence status

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How to cite this record

ethics.ai (15 June 2026), “Towards Auditing AI Systems in the Wild,” evidence record 927, https://ethics.ai/record/927 (originally published by arXiv).

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