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A suite of tools for a customized, end-to-end responsible AI experience.

The Responsible AI Toolbox is a comprehensive suite of tools designed to operationalize responsible AI practices throughout the AI lifecycle. It provides functionalities for model debugging, decision-making, fairness assessment, and error analysis. The toolbox offers a single-pane-of-glass experience through the Responsible AI Dashboard, allowing users to interactively connect insights from various tools to conduct holistic responsible AI assessments. Key components include error analysis widgets, explanation dashboards, and fairness dashboards. The architecture allows for customization based on unique tooling needs, including integration with external tools. Use cases range from identifying and mitigating model biases to explaining individual predictions and informing business decisions. The toolbox supports both classification and regression models, with metrics that are tailored to the model type.
The Responsible AI Toolbox is a comprehensive suite of tools designed to operationalize responsible AI practices throughout the AI lifecycle.
Explore all tools that specialize in explain model predictions. This domain focus ensures Responsible AI Toolbox delivers optimized results for this specific requirement.
Explore all tools that specialize in error analysis. This domain focus ensures Responsible AI Toolbox delivers optimized results for this specific requirement.
Facilitates the identification of error patterns by training surrogate decision trees on prediction errors. It allows users to explore error distributions across different feature segments.
Evaluates model performance across different sensitive groups, highlighting potential fairness issues based on chosen fairness metrics.
Offers both global and local explanations of model behavior using techniques like feature importance and individual prediction explanations.
Allows users to create custom workflows that integrate different tools within the toolbox to address specific responsible AI requirements.
Combines error analysis, fairness assessment, and explanation capabilities into a single dashboard for a holistic view of model behavior.
Install the raiwidgets package via pip: `pip install raiwidgets`
Import the necessary modules: `from raiwidgets import ErrorAnalysisDashboard, ExplanationDashboard, FairnessDashboard`
Prepare your data: dataset (features), true labels, and predictions.
Instantiate the desired dashboard: `ErrorAnalysisDashboard(dataset=X_test, true_y=y_test, pred_y=predictions, features=features)`
Run the dashboard in your notebook environment or host it locally using the provided port option.
Customize the dashboard by passing in parameters such as categorical features, metric, max_depth, and locale.
Explore the insights and visualizations generated by the dashboard to identify areas for improvement.
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