Recourse for reclamation: Chatting with generative language models
Jennifer Chien Kevin R. McKee Jackie Kay William Isaac
Researchers and developers increasingly rely on toxicity scoring to moderate
generative language model outputs, in settings such as customer service,
information retrieval, and content generation. However, toxicity scoring may
render pertinent information inaccessible, rigidify or "value-lock" cultural
norms, and prevent language reclamation processes, particularly for
marginalized people. In this work, we extend the concept of algorithmic
recourse to generative language models: we provide users a novel mechanism to
achieve their desired prediction by dynamically setting thresholds for toxicity
filtering. Users thereby exercise increased agency relative to interactions
with the baseline system. A pilot study ($n = 30$) supports the potential of
our proposed recourse mechanism, indicating improvements in usability compared
to fixed-threshold toxicity-filtering of model outputs. Future work should
explore the intersection of toxicity scoring, model controllability, user
agency, and language reclamation processes -- particularly with regard to the
bias that many communities encounter when interacting with generative language
models.