A176.
Overbye-Thompson,
H., & Rice, R. E. (2025). Understanding how users may work around
algorithmic bias. AI & Society. Open
access: https://doi.org/10.1007/s00146-025-02498-1
Algorithms
increasingly
mediate critical aspects of daily life across healthcare, hiring, and
social
media, shaping user experiences through automated decision-making
processes.
Yet, algorithmic bias, the systematic disadvantaging of certain groups
through
automated systems, has been widely documented across a variety of
algorithms.
Thus this study addresses the gap in understanding how users may
respond to
four epistemic categories of algorithm bias, depending on whether it
exists or
not, and is perceived or not. We apply the information systems concept
of
workarounds to characterize potential user responses to these
categories of
algorithmic bias. Then, we apply the Human–AI Interaction Theory of
Interactive
Media Effects to understand how users may detect bias through cue
routes and
develop workaround strategies through action routes. Our theoretical
framework
proposes how users' detection and workarounds may vary based on the
four
categories of bias. Understanding these adaptive strategies provides
crucial
insights for developing inclusive technologies and fostering
algorithmic
literacy, ultimately enhancing the ongoing negotiation between human
agency and
technological constraint in digital societies.