responsible-ai-toolbox

Why we included this project

Most model evaluation stops at a test-set accuracy number, but this toolbox goes further by giving you interactive Jupyter widgets and Python libraries for seeing how a model behaves up close. You can trace individual predictions to spot where errors cluster and which features drive the outcome, all from inside a notebook rather than a separate reporting tool. It also includes fairness checks, counterfactual examples, and causal analysis, which matters when regulators or your own stakeholders want an explanation for a decision rather than just a score. Teams working with tabular data, text, or images get a shared visual way to review models before shipping, and the dashboard layout is easy to pick up. Since everything runs locally under the MIT license, it slots into an existing ML workflow without much ceremony.