  Explainable AI (XAI) research has been booming, but the question "$\textbf{To
whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not
much of XAI is comprehensible to non-AI experts, who nonetheless, are the
primary audience and major stakeholders of deployed AI systems in practice. The
gap is glaring: what is considered "explained" to AI-experts versus non-experts
are very different in practical scenarios. Hence, this gap produced two
distinct cultures of expectations, goals, and forms of XAI in real-life AI
deployments.
  We advocate that it is critical to develop XAI methods for non-technical
audiences. We then present a real-life case study, where AI experts provided
non-technical explanations of AI decisions to non-technical stakeholders, and
completed a successful deployment in a highly regulated industry. We then
synthesize lessons learned from the case, and share a list of suggestions for
AI experts to consider when explaining AI decisions to non-technical
stakeholders.
