The document summarizes five "tribes" or categories of machine learning explainers: 1) The Featurists, who identify important features that a model relies on through methods like feature importance, selection, and correlation. 2) The Speculators, who examine how a model responds to changes in individual variables using techniques like partial dependence plots and individual conditional expectations. 3) The Localizers, who fit interpretable models locally to explain individual predictions using methods like LIME and anchors. 4) The Convoluters, who visualize important regions in images for convolutional neural networks. 5) The Trainalyzers, who identify training examples that most influenced a prediction using influence functions.