Error analysis in machine learning: going beyond predictive performance

Error analysis is crucial for developing trustworthy machine learning (ML) systems by focusing on understanding when, how, and why models fail, which goes beyond just predictive performance metrics like accuracy. The process involves various activities, including error cohort analysis, explainability, counterfactual analysis, and systematic testing, to ensure models are robust and reliable. Error cohort analysis helps identify performance discrepancies across different data subgroups, while global and local explanations clarify what features influence model predictions, offering insights for both practitioners and businesses. Counterfactual and adversarial analysis test the model's behavior under unforeseen conditions to highlight biases or vulnerabilities, and generating synthetic data can enhance model robustness by addressing underrepresented scenarios. Systematic testing, inspired by software engineering practices, is emphasized to proactively identify and correct errors, with tools like CheckList demonstrating improved testing outcomes in NLP models. Openlayer, a company focused on model debugging and validation, underscores the importance of these error analysis procedures in ML development pipelines.