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Computer Science > Robotics

arXiv:2505.05831 (cs)
[Submitted on 9 May 2025]

Title:Oh F**k! How Do People Feel about Robots that Leverage
Profanity?

Authors:Madison R. Shippy, Brian J. Zhang, Naomi T. Fitter
View a PDF of the paper titled Oh F**k! How Do People Feel about
Robots that Leverage Profanity?, by Madison R. Shippy and 2 other
authors
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    Abstract:Profanity is nearly as old as language itself, and
    cursing has become particularly ubiquitous within the last
    century. At the same time, robots in personal and service
    applications are often overly polite, even though past work
    demonstrates the potential benefits of robot norm-breaking. Thus,
    we became curious about robots using curse words in error
    scenarios as a means for improving social perceptions by human
    users. We investigated this idea using three phases of
    exploratory work: an online video-based study (N = 76) with a
    student pool, an online video-based study (N = 98) in the general
    U.S. population, and an in-person proof-of-concept deployment (N
    = 52) in a campus space, each of which included the following
    conditions: no-speech, non-expletive error response, and
    expletive error response. A surprising result in the outcomes for
    all three studies was that although verbal acknowledgment of an
    error was typically beneficial (as expected based on prior work),
    few significant differences appeared between the non-expletive
    and expletive error acknowledgment conditions (counter to our
    expectations). Within the cultural context of our work, the U.S.,
    it seems that many users would likely not mind if robots curse,
    and may even find it relatable and humorous. This work signals a
    promising and mischievous design space that challenges typical
    robot character design.

Comments: Under review for the 2025 IEEE RO-MAN Conference
Subjects: Robotics (cs.RO)
Cite as:  arXiv:2505.05831 [cs.RO]
          (or arXiv:2505.05831v1 [cs.RO] for this version)
          https://doi.org/10.48550/arXiv.2505.05831
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Naomi Fitter [view email]
[v1] Fri, 9 May 2025 06:58:43 UTC (1,439 KB)
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