Abstract: Identifying, visualizing, and mitigating machine learning model bias is provided. A system receives a feature of a plurality of features used by a model to generate output. The feature includes a plurality of categories, and the output comprises a plurality of types. The system identifies a metric used to evaluate a performance of the model and a threshold for the metric. The system determines a value for the metric for a category of the plurality of categories of the feature by comparing of a first number of values of a first type of the plurality of types output by the model for the category with a second number of values of the first type output by the model for the second category. The system generates a notification indicating the performance of the model responsive to a comparison of the value for the metric with the threshold for the metric.

Abstract: A system can include a data processing system that can include memory and one or more processors to generate, by a first model trained using machine learning and compatible with first data having a first type and second data having a second type, a first metric based on the first data and indicating a first fault probability in a second model, generate, by the first model, a second metric based on the second data and indicating a second fault probability in a third model, determine, based on the first metric and the second metric, that an aggregate model that includes the second model and the third model satisfies a heuristic indicating a third fault probability in the aggregate model, and instruct, in response to a determination that the aggregate model satisfies the heuristic, a user interface to present an indication that the aggregate model satisfies the heuristic.

Abstract: Methods for automatically generating documentation for a computer-implemented model are provided. In some embodiments, automatically generating documentation for a computer-implemented model includes receiving user input indicative of selection of the computer-implemented model, receiving user input indicative of selection of a documentation template including synthetic content placeholders, and automatically generating documentation for the computer-implemented model by automatically generating synthetic content for each of the synthetic content placeholders based on one or more characteristics of the computer-implemented model, and automatically populating the synthetic content placeholders with the synthetic content.