AI输入建议影响人类写作，反刻板印象建议能提升非刻板内容产出。
When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-Writing
- 通过单字提示词测试人类与AI协作时的性别偏见变化。
- 反刻板提示使反刻板故事比例显著上升，但主流仍是刻板叙事。
- 技术去偏仅部分有效，需结合人文干预改善人机协作伦理。
基于语言模型的系统会复制甚至放大训练数据中的社会偏见，导致生成文本或建议中出现规范性不当的刻板印象。然而，这些行为对人类使用系统时写作内容的影响尚不明确。本研究在共写场景下，通过测量414名参与者在面对英语单字语言模型预测建议时的写作反应，评估刻板与反刻板提示的影响。结果表明，反刻板建议有时显著提高了共写故事中反刻板内容的比例。尽管如此，具有刻板倾向的叙事仍占主导地位，说明单纯的技术去偏策略在缓解人机协作中的危害方面效果有限。
原文摘要 · Abstract (English)
AI-based systems such as language models have been shown to replicate and even amplify social biases reflected in their training data. Among other questionable behaviors, this can lead to AI-generated text--and text suggestions--that contain normatively inappropriate stereotypical associations. Little is known, however, about how this behavior impacts the writing produced by people using these systems. We address this gap by measuring how much impact stereotypes or anti-stereotypes in English single-word LM predictive text suggestions have on the stories that people write using those tools in a co-writing scenario. We find that ($n=414$), LM suggestions that challenge stereotypes sometimes lead to a significantly increased rate of anti-stereotypical co-written stories. However, despite this increased rate of anti-stereotypical stories, pro-stereotypical narratives still dominated the co-written stories, demonstrating that technical debiasing is only a partially effective strategy to alleviate harms from human-AI collaboration.
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