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microsoft/responsible-ai-toolbox

A suite of Python and web UI tools for assessing and debugging ML models for error patterns, fairness, interpretability and causal effects.

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What is it?

Responsible AI Toolbox is a collection of visualization dashboards and Python libraries for examining machine learning models and their data. Its main entry point is the Responsible AI dashboard, which brings together error analysis, fairness assessment, model interpretability, counterfactual analysis, causal analysis and data balance views in one interface for use in Jupyter notebooks.

The individual components are built on established open-source projects: Error Analysis finds data cohorts where a model underperforms, Fairlearn assesses group fairness metrics, InterpretML explains model predictions, DiCE generates counterfactuals, and EconML supports what-if causal questions. The dashboard is installed with pip as the raiwidgets package and works with models trained on numpy, pandas, or scipy sparse data, as well as PyTorch, TensorFlow and Keras models.

The repository also includes a companion ecosystem split across separate projects, including a mitigations library for cohort-specific fixes, an experiment tracker JupyterLab extension that integrates with MLflow, and a gender bias measurement library for NLP corpora.

Key features

  • 01Single dashboard combining error analysis, fairness, interpretability, counterfactuals and causal analysis.
  • 02Identifies data cohorts where a model's error rate is higher than the overall rate.
  • 03Computes group-fairness metrics across sensitive features.
  • 04Provides global and per-prediction explanations via InterpretML.
  • 05Installable with pip as raiwidgets and runs in Jupyter notebooks.
  • 06Supports models built with scikit-learn-style APIs, PyTorch, TensorFlow and Keras.

Use cases

  • Finding subgroups where a classifier performs poorly before deploying it.
  • Checking whether a model treats demographic groups unequally.
  • Explaining individual predictions to stakeholders reviewing an AI system.
  • Estimating how a change in inputs or a business action might affect outcomes.

Quick start

  1. 1

    Install the Python package from PyPI

    pip install raiwidgets
  2. 2

    Open the getting-started notebook from the repository examples

Things to know

  • The README's main quick start targets Jupyter notebooks, and you must restart the kernel after installing.
  • The project is split across three repositories, so related tools live outside this one.

In collections

Data science courses & tools

Topics

#responsible-ai#explainable-ai#fairness#model-debugging#machine-learning#jupyter#interpretability#causal-inference#data-analysis#data-science

FAQ

What is responsible-ai-toolbox?
A suite of Python and web UI tools for assessing and debugging ML models for error patterns, fairness, interpretability and causal effects. Responsible AI Toolbox is a collection of visualization dashboards and Python libraries for examining machine learning models and their data. Its main entry point is the Responsible AI dashboard, which brings together error analysis, fairness assessment, model interpretability, counterfactual analysis, causal analysis and data balance views in one interface for use in Jupyter notebooks.
Is responsible-ai-toolbox free to use? What license does it have?
responsible-ai-toolbox is open source under the MIT license. Check the license terms before using it in commercial products.
Is responsible-ai-toolbox still maintained?
Actively maintained. The last commit to responsible-ai-toolbox was on Nov 6, 2026.
What can I use responsible-ai-toolbox for?
Finding subgroups where a classifier performs poorly before deploying it. Checking whether a model treats demographic groups unequally. Explaining individual predictions to stakeholders reviewing an AI system. Estimating how a change in inputs or a business action might affect outcomes.
How do I install responsible-ai-toolbox?
Install the Python package from PyPI: pip install raiwidgets Open the getting-started notebook from the repository examples
Where can I get responsible-ai-toolbox?
The source code and installation instructions are on GitHub: https://github.com/microsoft/responsible-ai-toolbox

Summary generated by AI from the project's README. GitHub stats refresh daily.

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