Explainability Requires Interactivity

• URL: http://arxiv.org/abs/2109.07869v1
• Date: Thu, 16 Sep 2021 11:02:25 GMT
• Title: Explainability Requires Interactivity
• Authors: Matthias Kirchler, Martin Graf, Marius Kloft, Christoph Lippert
• Abstract summary: We introduce an interactive framework to understand the highly complex decision boundaries of modern vision models. It allows the user to exhaustively inspect, probe, and test a network's decisions.
• Score: 13.381840447825969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When explaining the decisions of deep neural networks, simple stories are tempting but dangerous. Especially in computer vision, the most popular explanation approaches give a false sense of comprehension to its users and provide an overly simplistic picture. We introduce an interactive framework to understand the highly complex decision boundaries of modern vision models. It allows the user to exhaustively inspect, probe, and test a network's decisions. Across a range of case studies, we compare the power of our interactive approach to static explanation methods, showing how these can lead a user astray, with potentially severe consequences.

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