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We use a deep learning based approach to predict whether a selected elementin a mobile UI screenshot will be perceived by users as tappable, based onpixels only instead of view hierarchies required by previous work. To helpdesigners better understand model predictions and to provide more actionabledesign feedback than predictions alone, we additionally use ML interpretabilitytechniques to help explain the output of our model. We use XRAI to highlightareas in the input screenshot that most strongly influence the tappabilityprediction for the selected region, and use k-Nearest Neighbors to present themost similar mobile UIs from the dataset with opposing influences ontappability perception.

Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis