We present a mixed-methods study of professional fiction writers' experiences with generative AI (genAI), primarily focused on queer and disabled writers. Queer and disabled writers are markedly more pessimistic than others about the impact of genAI on their industry, although pessimism is the majority attitude for all groups. We explore how genAI exacerbates existing causes of precarity for writers, reasons why writers are opposed to its use, and strategies used by marginalized fiction writers to safeguard their industry.

Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, the language of questions influences the language of responses. We develop a technique to correct for such influences and apply it to question-and-answer datasets. Surprisingly, such correction is not sufficient - those being deceptive react to prompting words in qualitatively different ways to those telling the truth. Accurate detection of deception in interrogation settings therefore requires modelling word use in both questions and answers.