AL360° Trustworthy AI Model Analysis SDK for Python

This package has been tested with Python 3.7, 3.8, 3.9 and 3.10

The AL360° Trustworthy AI Model Analysis SDK enables users to analyze their machine learning models in one API. Users will be able to analyze errors, explain the most important features, compute counterfactuals and run causal analysis using a single API.

Highlights of the package include:

• explainer.add() explains the model
• counterfactuals.add() computes counterfactuals
• error_analysis.add() runs error analysis
• causal.add() runs causal analysis

Supported scenarios, models and datasets

al360_trustworthyai supports computation of AL360° Trustworthy AI insights for scikit-learn models that are trained on pandas.DataFrame. The al360_trustworthyai accept both models and pipelines as input as long as the model or pipeline implements a predict or predict_proba function that conforms to the scikit-learn convention. If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (predict or predict_proba of scikit-learn), and pass that wrapper class to modules in al360_trustworthyai.

Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four responsible AI insights:-

The source code can be found here: https://github.com/affectlog360/affectlog360/tree/main/al360_trustworthyai