  As AI systems proliferate in society, the AI community is increasingly
preoccupied with the concept of AI Safety, namely the prevention of failures
due to accidents that arise from an unanticipated departure of a system's
behavior from designer intent in AI deployment. We demonstrate through an
analysis of real world cases of such incidents that although current vocabulary
captures a range of the encountered issues of AI deployment, an expanded
socio-technical framing will be required for a more complete understanding of
how AI systems and implemented safety mechanisms fail and succeed in real life.
