Inference and Learning from Data Learning

Most learning algorithms, including deep neural networks with many layers and parameters, act as black-box procedures where feature vectors at the input layer are transformed into label predictions at the output layer through a succession of nonlinear transformations. Given how prevalent learning-based systems are becoming in modern practice, including their use in fields such as medical diagnosis, autonomous systems, and even legal proceedings, it is necessary to have confidence in their predictions in order to ensure reliable, fair, and nondiscriminatory conclusions. For this reason, one needs to understand how classification results are attained, and what attributes in the input data have influenced the decisions most heavily. Questions of this type are addressed under the topic of explainability in machine learning.