  Knowledge can't be disentangled from people. As AI knowledge systems mine
vast volumes of work-related data, the knowledge that's being extracted and
surfaced is intrinsically linked to the people who create and use it. When
these systems get embedded in organizational settings, the information that is
brought to the foreground and the information that's pushed to the periphery
can influence how individuals see each other and how they see themselves at
work. In this paper, we present the looking-glass metaphor and use it to
conceptualize AI knowledge systems as systems that reflect and distort,
expanding our view on transparency requirements, implications and challenges.
We formulate transparency as a key mediator in shaping different ways of
seeing, including seeing into the system, which unveils its capabilities,
limitations and behavior, and seeing through the system, which shapes workers'
perceptions of their own contributions and others within the organization.
Recognizing the sociotechnical nature of these systems, we identify three
transparency dimensions necessary to realize the value of AI knowledge systems,
namely system transparency, procedural transparency and transparency of
outcomes. We discuss key challenges hindering the implementation of these forms
of transparency, bringing to light the wider sociotechnical gap and
highlighting directions for future Computer-supported Cooperative Work (CSCW)
research.
