explanability
Explanability, often used interchangeably with interpretability, refers to the degree to which a human can understand the cause of a decision made by a machine learning model. In simpler terms, it's about being able to explain why a model produced a particular output or prediction. This concept is crucial for building trust in artificial intelligence systems, especially in sensitive domains like healthcare, finance, and criminal justice where errors can have significant consequences.
There are various approaches to achieving explainability. Some models are inherently interpretable, such as linear regression
The importance of explainability stems from several factors. It facilitates debugging and model improvement by identifying