Machine Learning Interpretability and Explainability

Machine Learning (ML) interpretability and explainability are important concepts that refer to the ability of humans to understand and interpret the decisions made by machine learning models. These concepts have become increasingly important as machine learning models are being used in more critical applications, such as healthcare, finance, and criminal justice, where the decisions made by these models can have a significant impact on people's lives. One of the main challenges of ML interpretability and explainability is the complexity of the models. Machine learning models can be very complex, with many layers of neurons and thousands or even millions of parameters. This complexity can make it difficult for humans to understand how the model is making its decisions, which can be a problem when trying to explain the results to non-technical users.