Explaining Supervised Learning Models: A Preliminary Study on Binary Classifiers

The reach of artificial intelligence continues to grow, particularly with the expansion of machine learning techniques that capitalize on increased computing power. Such systems could have tremendous benefits by providing predictions and suggestions. However, they are limited by the fact that they offer incomplete explanations of their predictions to human decision makers. The objective of this work was to summarize general information that could help users make judgments about whether a system is trustworthy and whether the system’s training “makes sense.” A preliminary study was summarized to show the importance of iterative design and testing for visualizing explanations.