Software Diagnostics and Observability Institute

From residuals, validation sets, and reasoning traces to model monitoring, mechanistic interpretability, foundation-model evaluation, and agentic observability

PDF: https://www.dumpanalysis.org/files/History_of_ML_and_AI_Diagnostics_Obse...

Machine learning and artificial intelligence systems may execute without any software exception and still produce incorrect results. A model may depend on an accidental feature of its training data, or an agent may return the expected answer after performing an incorrect sequence of actions. To diagnose such problems, we need suitable artifacts and methods for their analysis.

Here we consider the historical development of ML and AI diagnostics, observability, and debugging. We follow related threads of thinking from statistical residuals, feedback, and symbolic reasoning to training diagnostics, model monitoring, mechanistic interpretability, and agentic observability. We also examine data leakage, uncertainty, reproducibility, robustness, and diagnostic lessons from consequential failures.

From our pattern-oriented viewpoint, this history describes an expanding collection of observable structures and methods for their interpretation. Datasets, gradients, checkpoints, explanations, and traces provide different kinds of evidence. We consider what these artifacts make visible, how they help distinguish possible mechanisms, and where their diagnostic usefulness ends. Particular attention is paid to the relationships among models, data, software, people, and the surrounding environment.

The book includes 35 chapters, 143 references, a selected chronology, a glossary, and a taxonomy of diagnostic evidence. It is intended for software engineers, machine learning practitioners, researchers, and readers interested in the development of diagnostic thinking.