microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

What it solves

It provides a comprehensive suite of tools to assess, develop, and deploy AI systems safely and ethically. It helps developers and stakeholders identify model errors, diagnose why they occur, detect fairness issues across different demographic groups, and understand the reasoning behind individual predictions to make better data-driven decisions.

How it works

The toolbox integrates several specialized open-source libraries into a set of visualization widgets and dashboards. It uses Error Analysis to find data cohorts where the model underperforms, Fairlearn for fairness assessment, InterpretML for model interpretability, DiCE for counterfactual analysis (showing how small changes in input would change a prediction), and EconML for causal analysis to distinguish correlation from causation.

Who it’s for

AI practitioners, model developers, and business stakeholders who need to debug their machine learning models, ensure fairness, and perform holistic model assessment across tabular, text, and vision data.

Highlights

  • Responsible AI Dashboard: A single interface that combines error analysis, fairness, interpretability, and causal analysis.
  • Customizable Workflows: Allows users to design specific debugging paths, such as moving from model overview to error analysis and then to data exploration.
  • Broad Model Support: Compatible with models using Scikit-learn conventions, as well as PyTorch, TensorFlow, and Keras.
  • Multi-modal Examples: Includes pre-built notebooks for debugging classification and regression models across text, vision, and tabular datasets.

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