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

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Richards2023DEIHRI.pdf (473.65 KB)

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https://iral.cs.umbc.edu/Pubs/Richards2023DEIHRI.pdf

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http://hdl.handle.net/11603/28085

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Author/Creator ORCID

https://orcid.org/0000-0001-5744-8736
 https://orcid.org/0000-0003-1383-8120

Date

2023

Type of Work

journal articles
preprints
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Citation of Original Publication

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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.