Seven simple steps for log analysis in AI systems

TL;DR

This paper proposes a standardized pipeline for log analysis in AI systems, illustrated with code examples and guidance to improve rigor and reproducibility.

Contribution

It introduces a comprehensive, best-practices-based framework for log analysis in AI, including code and guidance to address current inconsistencies.

Findings

Provides a detailed pipeline for log analysis in AI systems.

Includes concrete code examples in the Inspect Scout library.

Highlights common pitfalls and best practices.

Abstract

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.