  Content Warning: This paper contains examples of misgendering and erasure
that could be offensive and potentially triggering.
  Misgendering, the act of incorrectly addressing someone's gender, inflicts
serious harm and is pervasive in everyday technologies, yet there is a notable
lack of research to combat it. We are the first to address this lack of
research into interventions for misgendering by conducting a survey of
gender-diverse individuals in the US to understand perspectives about automated
interventions for text-based misgendering. Based on survey insights on the
prevalence of misgendering, desired solutions, and associated concerns, we
introduce a misgendering interventions task and evaluation dataset,
MisgenderMender. We define the task with two sub-tasks: (i) detecting
misgendering, followed by (ii) correcting misgendering where misgendering is
present in domains where editing is appropriate. MisgenderMender comprises 3790
instances of social media content and LLM-generations about non-cisgender
public figures, annotated for the presence of misgendering, with additional
annotations for correcting misgendering in LLM-generated text. Using this
dataset, we set initial benchmarks by evaluating existing NLP systems and
highlighting challenges for future models to address. We release the full
dataset, code, and demo at
https://tamannahossainkay.github.io/misgendermender/.
