Description: Local interpretability refers to the ability to explain individual predictions made by an artificial intelligence (AI) model. Unlike global interpretability, which seeks to understand the overall behavior of a model, local interpretability focuses on breaking down and clarifying why a model has made a specific decision in a particular case. This is crucial in applications where automated decisions can have a significant impact on people’s lives, such as in healthcare, finance, or justice. Local interpretability allows users and developers to understand the features that influenced the prediction, facilitating the identification of biases or errors in the model. Furthermore, it provides greater trust in AI systems, as users can see and understand the reasons behind decisions, which is essential for the acceptance and adoption of these technologies. Tools like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are examples of approaches that enable local interpretability, offering explanations that are accessible and understandable to users, even if they do not have deep technical knowledge.
