用AI对话引导识别残障歧视，发现正向引导更有效且易被接受
"I followed what felt right, not what I was told": Autonomy, Coaching, and Recognizing Bias Through AI-Mediated Dialogue
- 通过AI对话分四组实验，对比不同引导方式对偏见认知的影响
- 带偏见引导的对话虽提升辨识度，但引发更多负面情绪
- 中立包容型引导更易被接纳，适合构建有温度的干预系统
残障歧视性微侵犯在日常互动中仍普遍存在，但帮助人们识别它们的干预手段有限。我们开展一项实验，测试AI对话如何影响人们对残障歧视的认知。160名参与者经历前测、干预和后测，分为四组：由AI引导关注偏见（Bias-Directed）、引导包容（Neutral-Directed）、自主对话（Self-Directed）及纯文本阅读（Reading）。参与者评估情景在社会正常性与情感影响上的程度；对话组还提供定性反思。定量结果显示，对话类条件显著优于阅读组，但轨迹分化：带有偏见引导的组提升了对偏见与中立情境的区分能力，却导致整体情绪更负面；包容或无引导组则保持更平衡状态，而阅读组改善有限甚至出现退步。定性分析显示，偏见引导常被拒绝，而包容引导被视作可采纳的思维支架。本研究贡献了一个经验证的短剧语料库、一个AI干预平台，并揭示了对话系统在整合偏见提示时面临的权衡问题。
原文摘要 · Abstract (English)
Ableist microaggressions remain pervasive in everyday interactions, yet interventions to help people recognize them are limited. We present an experiment testing how AI-mediated dialogue influences recognition of ableism. 160 participants completed a pre-test, intervention, and a post-test across four conditions: AI nudges toward bias (Bias-Directed), inclusion (Neutral-Directed), unguided dialogue (Self-Directed), and a text-only non-dialogue (Reading). Participants rated scenarios on standardness of social experience and emotional impact; those in dialogue-based conditions also provided qualitative reflections. Quantitative results showed dialogue-based conditions produced stronger recognition than Reading, though trajectories diverged: biased nudges improved differentiation of bias from neutrality but increased overall negativity. Inclusive or no nudges remained more balanced, while Reading participants showed weaker gains and even declines. Qualitative findings revealed biased nudges were often rejected, while inclusive nudges were adopted as scaffolding. We contribute a validated vignette corpus, an AI-mediated intervention platform, and design implications highlighting trade-offs conversational systems face when integrating bias-related nudges.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。