  Efforts to promote fairness, accountability, and transparency are assumed to
be critical in fostering Trust in AI (TAI), but extant literature is
frustratingly vague regarding this 'trust'. The lack of exposition on trust
itself suggests that trust is commonly understood, uncomplicated, or even
uninteresting. But is it? Our analysis of TAI publications reveals numerous
orientations which differ in terms of who is doing the trusting (agent), in
what (object), on the basis of what (basis), in order to what (objective), and
why (impact). We develop an ontology that encapsulates these key axes of
difference to a) illuminate seeming inconsistencies across the literature and
b) more effectively manage a dizzying number of TAI considerations. We then
reflect this ontology through a corpus of publications exploring fairness,
accountability, and transparency to examine the variety of ways that TAI is
considered within and between these approaches to promoting trust.
