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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Sep 02, 2020 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mingxi Cheng, Department of Electrical and Computer Engineering, University of Southern California

    Talk Title: There Is Hope After All: Quantifying Trustworthiness in Neural Networks

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: Artificial Intelligence (AI) plays a fundamental role in the modern world, especially when used as an autonomous decision maker. One common concern nowadays is "how trustworthy the AIs are." Human operators follow a strict educational curriculum and performance assessment that could be exploited to quantify how much we entrust them. To quantify the trust of AI decision makers, we must go beyond task accuracy especially when facing limited, incomplete, misleading, controversial or noisy datasets. Toward addressing these challenges, we describe DeepTrust, a Subjective Logic (SL) inspired framework that constructs a probabilistic logic description of an AI algorithm and takes into account the trustworthiness of both dataset and inner algorithmic workings. DeepTrust identifies proper multi-layered neural network (NN) topologies that have high projected trust probabilities, even when trained with untrusted data. We show that uncertain opinion of data is not always malicious while evaluating NN's opinion and trustworthiness, whereas the disbelief opinion hurts trust the most. Also trust probability does not necessarily correlate with accuracy. DeepTrust also provides a projected trust probability of NN's prediction, which is useful when the NN generates an over-confident output under problematic datasets. These findings open new analytical avenues for designing and improving the NN topology by optimizing opinion and trustworthiness, along with accuracy, in a multi-objective optimization formulation, subject to space and time constraints.

    Biography: Mingxi Cheng is currently a 3rd year Ph.D. student at the University of Southern California (USC) under the supervision of Prof. Paul Bogdan and Prof. Shahin Nazarian. She received her B.S. degree from Beijing University of Posts and Telecommunications (BUPT), China in 2016 and M.S. degree from Duke University in 2018. Her research interests include deep learning, natural language processing, and artificial intelligence.

    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQ

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQ

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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