Anti-stereotypical Predictive Text Suggestions Do Not Reliably Yield Anti-stereotypical Writing
<|reference_start|>Anti-stereotypical Predictive Text Suggestions Do Not Reliably Yield Anti-stereotypical Writing: AI-based systems such as language models can replicate and amplify social biases reflected in their training data. Among other questionable behavior, this can lead to LM-generated text--and text suggestions--that contain normatively inappropriate stereotypical associations. In this paper, we consider the question of how "debiasing" a language model impacts stories that people write using that language model in a predictive text scenario. We find that (n=414), in certain scenarios, language model suggestions that align with common social stereotypes are more likely to be accepted by human authors. Conversely, although anti-stereotypical language model suggestions sometimes lead to an increased rate of anti-stereotypical stories, this influence is far from sufficient to lead to "fully debiased" stories.<|reference_end|>
@article{baumler2024anti-stereotypical,
    title={Anti-stereotypical Predictive Text Suggestions Do Not Reliably Yield
  Anti-stereotypical Writing},
    author={Connor Baumler, Hal Daum'e III},
    journal={arXiv preprint arXiv:2409.20390},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20390},
    primaryClass={cs.CL}
}