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    Machine Learning Security as a Source of Unfairness in Human-Robot Interaction

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    Richards2023DEIHRI.pdf (473.6Kb)
    Links to Files
    https://iral.cs.umbc.edu/Pubs/Richards2023DEIHRI.pdf
    Permanent Link
    http://hdl.handle.net/11603/28085
    Collections
    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Student Collection
    Metadata
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    Author/Creator
    Richards, Luke E.
    Matuszek, Cynthia
    Author/Creator ORCID
    https://orcid.org/0000-0001-5744-8736
    https://orcid.org/0000-0003-1383-8120
    Date
    2023
    Type of Work
    3 pages
    Text
    journal articles
    preprints
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    UMBC Interactive Robotics and Language Lab
    Abstract
    Machine learning models that sense human speech, body placement, and other key features are commonplace in human-robot interaction. However, the deployment of such models in themselves is not without risk. Research in the security of machine learning examines how such models can be exploited and the risks associated with these exploits. Unfortunately, the threat models of risks produced by machine learning security do not incorporate the rich sociotechnical underpinnings of the defenses they propose; as a result, efforts to improve the security of machine learning models may actually increase the difference in performance across different demographic groups, yielding systems that have risk mitigation that work better for one group than another. In this work, we outline why current approaches to machine learning security present DEI concerns for the human-robot interaction community and where there are open areas for collaboration.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.