“海滩”到“婊子”：YouTube上儿童内容的无意不安全转录
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube
摘要：
在过去的几年里，YouTube Kids已经成为儿童娱乐电视的极具竞争力的替代品之一。因此，YouTube Kids的内容应该受到额外的审查，以确保儿童的安全。虽然关于检测儿童攻击性或不适当内容的研究正在取得进展，但目前很少或根本没有研究人工智能应用程序在多大程度上可以（意外地）引入不适合儿童的内容。在本文中，我们提出了一个新的（和令人不安的）发现，众所周知的自动语音识别（ASR）系统可能会产生文本内容非常不适合孩子们，而转录YouTube儿童的视频。我们把这种现象称为\emph{inappropriate content hallucination}。我们的分析表明，这种幻觉远非偶然，ASR系统通常会以很高的信心产生它们。我们发布了首个此类音频数据集，现有的最先进的ASR系统会为儿童产生不适当的内容。此外，我们还证明了其中一些错误可以使用语言模型来修复。
英文摘要：
Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety. While research on detecting offensive or inappropriate content for kids is gaining momentum, little or no current work exists that investigates to what extent AI applications can (accidentally) introduce content that is inappropriate for kids. In this paper, we present a novel (and troubling) finding that well-known automatic speech recognition (ASR) systems may produce text content highly inappropriate for kids while transcribing YouTube Kids' videos. We dub this phenomenon as \emph{inappropriate content hallucination}. Our analyses suggest that such hallucinations are far from occasional, and the ASR systems often produce them with high confidence. We release a first-of-its-kind data set of audios for which the existing state-of-the-art ASR systems hallucinate inappropriate content for kids. In addition, we demonstrate that some of these errors can be fixed using language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
