Evaluating AI Systems Before Deployment

This paper examines why evaluating AI systems before deployment is essential for understanding their reliability, limitations, and potential failure modes. Strong benchmark performance does not necessarily demonstrate that an AI system will behave reliably in unfamiliar, adversarial, or changing environments. The paper explores six areas of AI evaluation: the limitations of benchmark performance, deceptive or misleading behavior, system-level failure modes, adversarial testing, uncertainty handling, and post-deployment monitoring. It emphasizes that evaluations should examine the complete system, including its tools, permissions, data sources, and operating environment. Evaluation results should be treated as evidence rather than guarantees, and deployment decisions should account for unresolved risks and the potential consequences of system failure. Publication status: Unpublished preprint. Not peer-reviewed.