The AI Explainability 360 (AIX360) toolkit is a Python library that offers a wide range of explanation types as well as some explainability metrics. AIX360 offers excellent guidance material, an interactive demo as well as developer tutorials. What’s particularly good about this material is that it stimulates reflection on which type of explanation is appropriate, not only from a technical point of view, but also with respect to the target explainer and explainee. Read more...

ELI5 (“Explain Like I’m 5”) provides model-specific support for models from scikit-learn, lightning, decision tree ensembles using the xgboost, LightGBM, CatBoost libraries. ELI5 mainly provides convenient wrappers to couple the feature importance coefficients that these libraries already provide with feature names, as well as convenient ways to visualize importances, e.g. by highlighting words in a text. For Keras image classifiers an implementation of the gradient-based Grad-CAM visualizations is offered, but the TensorFlow V2 backend is not supported. Read more...

The documentation of fairlearn is excellent and provides a good introduction to the topic of fairness in AI. It is emphasized that fairness algorithms are no plug-and-play technical solutions, but require serious thought about the context of the data and the problem at hand. Fairness is a fundamentally sociotechnical challenge and cannot be solved with technical tools alone. They may be helpful for certain tasks such as assessing unfairness through various metrics, or to mitigate observed unfairness when training a model. Read more...

Interpret-Text is an extension of InterpretML , specifically for several text models. Three modules are provided: ClassicalTextExplainer, UnifiedInformationExplainer and IntrospectiveRationaleExplainer. Classical Text Explainer The ClassicalTextExplainer supports linear models from sklearn with a coefs_ call and tree-based models for which feature_importances_ is defined. ClassicalTextExplainer includes a NLP pipeline from preprocessing to hyperparameter tuning, so it accepts raw text data as input. The default pipeline uses a unigram bag-of-words model. Elements of the pipeline can be replaced if desired. Read more...

The SHAP package is built on the concept of a Shapley value and can generate explanations model-agnostically. So it only requires input and output values, not model internals: SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. (README) Additionally, this package also contains several model-specific implementations of Shapley values that are optimized for a particular machine learning model and sometimes even for a particular library. Read more...

This library provides a separate predict() function for scikit-learn tree-based models (so also ensembles) that outputs a prediction with interpretable elements of the shape prediction = bias + feature_1_contribution + ... + feature_n_contribution. That is, it turns these tree-based models into a white box , where we can inspect how much each feature contributes to the predicted value (in the case of regression) or how much it contributes to the estimated probability of a class (given classification). Read more...