发表机构
Swinburne University of Technology（斯威本科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对儿童AI中手语到文本翻译的安全接口，提出聋人知情的部署前审计方法，包括故障分类、场景模式、比较条件和结果指标，以澳手语为首个案例。
AI 中文摘要
自动手语翻译（SLT）已进入消费产品，将美国手语转换为英语文本，用于听写、消息传递以及向会话助手提出的查询。面向儿童的AI和平台信任与安全工具基于文本进行决策，使用针对未成年账户的过滤器和为聊天消息评分的诱骗分类器。因此，使用SLT的听障儿童通过这些翻译到达这些安全措施。我们发现没有公开记录的系统对两者进行联合评估，且领先的已部署SLT模型既未在18岁以下手语者上进行训练，也未进行正式评估。改变否定、参与者角色、保密性、紧迫性或寻求帮助的翻译错误可能在不影响流畅性的情况下改变安全决策。本文提出了一种由聋人知情的部署前审计该边界的方法，包括故障分类法、消毒场景模式、四种比较条件和四项结果指标。澳手语是计划中的首个案例研究。
英文摘要
Automatic sign language translation (SLT) has entered consumer products, turning American Sign Language into English text for dictation, messaging, and queries put to a conversational assistant. Child-facing AI and platform trust-and-safety tooling decide on text, using filters on minor accounts and grooming classifiers that score chat messages. A signing child who uses SLT therefore reaches these safeguards through a translation. We found no publicly documented system in which the two have been jointly evaluated, and the leading deployed SLT model was neither trained nor formally evaluated on signers under 18. Errors that alter negation, participant roles, secrecy, urgency or help-seeking could change a safety decision without disturbing fluency. This paper proposes a Deaf-informed pre-deployment audit of that boundary, with a failure taxonomy, a sanitised scenario schema, four comparison conditions, and four outcome measures. Auslan is the planned first case study.
Comments5 pages, 1 figure. Accepted as a poster at the NeurIPS 2026 Workshop on Child Safety in AI (non-archival)
