Course Overview Visual explanations have traditionally acted as rationales used to justify the decisions made by machine learning systems. With the advent of large-scale neural networks, the role of visual explanations has been to shed interpretability on black-box models. We view this role as the process for the network to answer the question `Why P?’, where P is a trained network’s prediction. Recently however, with increasingly capable models, the role of explainability has expanded. Neural networks are asked to justify `What if?’ counterfactual and `Why P, rather than Q?’ contrastive question modalities that the network did not explicitly train to answer. This allows explanations to act as reasons to make further prediction. The short course provides a principled and rational introduction into Explainability within machine learning and justifies them as reasons to make decisions. Such a reasoning framework allows for robust machine learning as well as trustworthy AI to be accepted in everyday lives. Applications like robust recognition, image quality assessment, visual saliency, anomaly detection, out-of-distribution detection, adversarial image detection, seismic interpretation, semantic segmentation, and machine teaching among others will be discussed. Day One: Lectures 1 through 4
