  We report on experiences with implementing conversational agents in the
recruitment domain based on a machine learning (ML) system. Recruitment
chatbots mediate communication between job-seekers and recruiters by exposing
ML data to recruiter teams. Errors are difficult to understand, communicate,
and resolve because they may span and combine UX, ML, and software issues. In
an effort to improve organizational and technical transparency, we came to rely
on a key contact role. Though effective for design and development, the
centralization of this role poses challenges for transparency in sustained
maintenance of this kind of ML-based mediating system.
