The Responsible AI Toolbox provides a suite of tools for developers and stakeholders to understand, assess, and debug AI systems for responsible development and deployment. It offers interactive dashboards and libraries to identify model errors, diagnose performance issues across data cohorts, understand prediction drivers, and evaluate fairness.

How It Works

The toolbox integrates several open-source libraries to provide a holistic view of AI model behavior. It leverages Error Analysis for cohort-based error identification, Fairlearn for fairness metrics, InterpretML for model interpretability, DiCE for counterfactual explanations, and EconML for causal analysis. This modular approach allows users to create customizable workflows for debugging and decision-making.

Quick Start & Requirements

• Install via pip: pip install raiwidgets
• Requires Python. Restart Jupyter kernel after installation.
• Links to documentation and examples: Getting Started, Tour

Highlighted Details

• Integrates Error Analysis, Fairness, Interpretability, Counterfactuals, and Causal Inference into a single dashboard.
• Supports tabular, text, and vision model debugging.
• Compatible with models trained on NumPy arrays, Pandas DataFrames, and Scikit-learn compatible models/pipelines.
• Can be used with API-based models like Azure Cognitive Services.

Maintenance & Community

• Maintained by a team of Microsoft engineers.
• No explicit community links (Discord/Slack) are provided in the README.

Licensing & Compatibility

• The primary repository is licensed under the MIT License.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

• The README mentions a separate "Responsible-AI-Toolbox-Mitigations Repository" for mitigation techniques, implying this core toolbox focuses primarily on assessment and diagnosis.
• While supporting deep learning frameworks, specific integration details or performance benchmarks for these are not detailed.