Machine Learning

The behavior of a machine learning model can be influenced by its training data, selected features, model architecture, training process, parameters, hyperparameters, and deployed model version. A Machine Learning Expert Witness can examine datasets, feature definitions, training procedures, model architecture, model parameters and hyperparameters, validation results, performance metrics, version histories, predictions, logs, testing records, and technical documentation to evaluate the machine learning methodology and performance at issue.

Changes in model performance can arise from data quality, feature selection, data leakage, overfitting, training choices, validation methods, deployment conditions, or differences between model versions. Predictive models, neural networks, and other machine learning systems can therefore require comparison of training and evaluation data, model configurations, validation results, and deployed outputs. A Machine Learning Expert Witness can analyze those relationships to help counsel evaluate how documented changes in data, features, training, validation, or model versions relate to differences in predictions or performance while distinguishing machine learning methodology from broader artificial intelligence, data-science analysis, statistical methodology, software implementation, or legal conclusions concerning an automated decision.