Transparency in Maintenance of Recruitment Chatbots
This addresses transparency issues in ML-based recruitment systems for organizations, but it is incremental as it builds on existing practices without introducing new methods.
The study tackled the problem of maintaining transparency in recruitment chatbots that mediate between job-seekers and recruiters, finding that while a key contact role improved transparency during design and development, it posed challenges for sustained maintenance due to centralization.
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.