SHAP is a Python library that provides unified, consistent, and interpretable explanations for a wide range of models. It employs Shapley values from cooperative game theory to attribute the contribution of each feature to a model's output.

LIME (Local Interpretable Model-agnostic Explanations) is a Python library designed to explain the predictions of machine learning models. It generates local interpretable models that approximate the behavior of the underlying complex model for a specific instance.

Fairness Indicators is a TensorFlow extension for assessing and improving fairness in machine learning models. It provides metrics, visualizations, and tools to evaluate model fairness across different subgroups in the data.

AI Fairness 360 is an open-source toolkit developed by IBM that contains a comprehensive set of algorithms and metrics for addressing bias and fairness concerns in machine learning models. It supports pre-processing, in-processing, and post-processing techniques.

InterpretML is a Python library that simplifies the process of interpreting machine learning models. It provides a unified interface for various interpretability techniques, making it easy to explore and understand model predictions.

Fairlearn is a Python library developed by Microsoft for assessing and mitigating unfairness in machine learning models. It includes tools for visualizing and mitigating disparities in predictive performance across different groups.

The What-If Tool is an interactive visual interface for exploring and understanding machine learning models. It allows users to analyze model behavior, investigate trade-offs, and assess fairness in predictions.

EthicalML is a Python library designed to promote ethical considerations in machine learning. It provides tools for model fairness, transparency, and interpretability. EthicalML emphasizes practical implementations of ethical AI principles, allowing users to assess and enhance the ethical aspects of their machine learning models.