Model Interpretability Part 1: The Importance and Approaches

Machine learning models, while powerful in making predictions and aiding decision-making, often suffer from a lack of interpretability, making it challenging for humans to understand their processes and outcomes. As these models, particularly complex ones like neural networks, become central to high-stakes decisions in industries such as banking and insurance, the need for interpretability becomes critical to ensure trust and compliance. The text outlines different approaches to model interpretability, including intrinsic and post hoc methods, model-specific and model-agnostic tools, and global and local interpretability frameworks. Intrinsic methods focus on the model's structure, while post hoc methods analyze the model after training. Model-agnostic interpretability offers flexibility to apply interpretative methods across various models, while global interpretability seeks to understand the entire model's decision-making process, and local interpretability focuses on individual predictions. This first part of a series emphasizes the importance of understanding and applying these interpretability methods to ensure transparency and reliability in the use of machine learning models.