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Exploring Conceptual Soundness with TruLens
Anupam Datta · Matt Fredrikson · Klas Leino · Kaiji Lu · Shayak Sen · Ricardo C Shih · Zifan Wang

Wed Dec 08 09:20 AM -- 09:35 AM (PST) @
Event URL: https://truera.github.io/neurips-demo-2021/ »

As machine learning has become increasingly ubiquitous, there has been a growing need to assess the trustworthiness of learned models. One important aspect to model trust is conceptual soundness, i.e., the extent to which a model uses features that are appropriate for its intended task. We present TruLens, a new cross-platform framework for explaining deep network behavior. In our demonstration, we provide an interactive application built on TruLens that we use to explore the conceptual soundness of various pre-trained models. Throughout the presentation, we take the unique perspective that robustness to small-norm adversarial examples is a necessary condition for conceptual soundness; we demonstrate this by comparing explanations on models trained with and without a robust objective. Our demonstration will focus on our end-to-end application, which will be made accessible for the audience to interact with; but we will also provide details on its open-source components, including the TruLens library and the code used to train robust networks.

Author Information

Anupam Datta (Carnegie Mellon University)
Matt Fredrikson (Carnegie Mellon University)
Klas Leino (Carnegie Mellon University)

I'm a researcher at CMU focused on studying the weaknesses and vulnerabilities of deep learning; I works to improve DNN security, transparency, and privacy

Kaiji Lu (Carnegie Mellon University)
Shayak Sen (TruEra, Inc.)
Ricardo C Shih (Truera)
Zifan Wang (Carnegie Mellon University)

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