Model Interpretability

Model interpretability refers to the ability to understand and explain the decisions made by a machine learning model, providing insights into its inner workings and the factors that influence its predictions. As AI becomes increasingly pervasive in high-stakes applications, model interpretability is crucial for building trust, ensuring accountability, and identifying potential biases in complex models, making it a vital area of research in the tech community.