title: Recourse for reclamation: Chatting with generative language models
summary: 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.
paper_authors._id: 65fd02aabe8494c6be3f1fba
paper_authors.name: Jennifer Chien
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-22T11:49:59.119Z
paper_authors._id: 65fd7039cce786577103dcfe
paper_authors.avatarUrl: /avatars/7ebc5ec9d7851ca1a0d33cc0bc703f81.svg
paper_authors.fullname: Jennifer Chien
paper_authors.type: user
paper_authors.user: thechiennifer
paper_authors._id: 65fd02aabe8494c6be3f1fbb
paper_authors.name: Kevin R. McKee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T10:32:06.717Z
paper_authors._id: 624911a1edf42bf849d0ffc5
paper_authors.avatarUrl: /avatars/fdf70a412b2b90d0407dc6adb484319f.svg
paper_authors.fullname: Kevin Mckee
paper_authors.type: user
paper_authors.user: Kmckee95
paper_authors._id: 65fd02aabe8494c6be3f1fbc
paper_authors.name: Jackie Kay
paper_authors._id: 65fd02aabe8494c6be3f1fbd
paper_authors.name: William Isaac
paper_title: Recourse for reclamation: Chatting with generative language models
paper_summary: 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.
paper_ai_summary: A mechanism for dynamic toxicity thresholding in generative language models enhances user agency and usability compared to fixed thresholds.
paper_ai_keywords: generative language models toxicity scoring algorithmic recourse dynamic thresholds fixed-threshold toxicity-filtering user agency language reclamation bias