  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.
