Seven simple steps for log analysis in AI systems
This provides a foundational framework for researchers to systematically analyze AI system logs, though it is incremental as it builds on existing practices.
The paper tackles the lack of standardized methods for analyzing logs in AI systems by proposing a seven-step pipeline based on best practices, illustrated with code examples in the Inspect Scout library to enable rigorous and reproducible analysis.
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intended. Researchers have started developing methods for log analysis, but a standardised approach is still missing. Here we suggest a pipeline based on current best practices. We illustrate it with concrete code examples in the Inspect Scout library, provide detailed guidance on each step, and highlight common pitfalls. Our framework provides researchers with a foundation for rigorous and reproducible log analysis.