Top 12 AI Explainability 360 alternatives

Aequitas is an open-source bias audit toolkit designed to evaluate machine learning models for discrimination and fairne...

AI Fairness 360 (AIF360) is an open-source toolkit that provides a suite of fairness metrics, bias detection algorithms,...

Alibi is an open-source Python library designed for interpreting and explaining machine learning models, with a focus on...

Captum is a model interpretability library for PyTorch that provides tools to understand and debug deep learning models....

Fairlearn is a Python toolkit designed to help developers assess and mitigate unfairness in machine learning models. It ...

InterpretML is an open-source toolkit that enables users to train interpretable machine learning models (like decision t...

Lightly is a Python framework for self-supervised learning on image data, enabling users to train models without labeled...

LOFO Importance is a Python library that evaluates feature importance for machine learning models by iteratively excludi...

mljar-supervised is an AutoML Python package designed for tabular data, automating tasks like feature engineering, hyper...

Quantus is an open-source toolkit designed to evaluate and benchmark explanation methods for neural networks, focusing o...

SHAP (SHapley Additive exPlanations) is a Python library that provides interpretable explanations for machine learning m...

SHAPash is a Python library that simplifies the interpretation of machine learning models by generating interactive visu...