ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions

Abstract

We present ScrutinAI, a Visual Analytics approach to exploit semantic understanding for deep neural network (DNN) predictions analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g. autonomous driving or healthcare, have a high demand for detecting and mitigating data- and model-inherent shortcomings. Our approach aims to help analysts use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI therefore includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, and data filtering with textual queries on descriptive meta data. The tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the ''black-box character'' of the DNN and thus support model improvement and generation of a safety argumentation for AI applications. We present a case study on the investigation of DNN models for pedestrian detection from the automotive domain.