Description: Model explainability refers to the degree to which a human can understand the cause of a decision made by a machine learning model. This concept is fundamental in the field of artificial intelligence, as it allows users and developers to interpret and trust automated decisions. Explainability focuses on breaking down the internal processes of a model, facilitating the understanding of how predictions are generated and what factors influence them. This is especially relevant in critical applications, such as healthcare, law, and finance, where decisions can have a significant impact on people’s lives. A lack of transparency in machine learning models can lead to distrust and limited adoption of these technologies. Therefore, explainability not only enhances user trust but also helps identify biases and errors in models, promoting a more ethical and responsible use of artificial intelligence. In summary, model explainability is an essential component for the acceptance and development of machine learning systems, ensuring that decisions made by these models are understandable and justifiable to humans.
