Model Interpretation

Model interpretation focuses on understanding how machine learning models arrive at their predictions, aiming to increase transparency and trust in their use. Current research emphasizes distinguishing between model-centric ("sensitive") and task-centric ("decisive") patterns in interpretations, exploring various methods including post-hoc and self-interpretable approaches applied to diverse architectures like convolutional neural networks and tree ensembles. This field is crucial for ensuring responsible AI deployment across scientific domains and practical applications, particularly in high-stakes areas like healthcare and finance, by providing insights into model behavior and identifying potential biases or flaws.