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Efforts to promote fairness, accountability, and transparency are assumed tobe critical in fostering Trust in AI (TAI), but extant literature isfrustratingly vague regarding this 'trust'. The lack of exposition on trustitself suggests that trust is commonly understood, uncomplicated, or evenuninteresting. But is it? Our analysis of TAI publications reveals numerousorientations which differ in terms of who is doing the trusting (agent), inwhat (object), on the basis of what (basis), in order to what (objective), andwhy (impact). We develop an ontology that encapsulates these key axes ofdifference to a) illuminate seeming inconsistencies across the literature andb) more effectively manage a dizzying number of TAI considerations. We thenreflect this ontology through a corpus of publications exploring fairness,accountability, and transparency to examine the variety of ways that TAI isconsidered within and between these approaches to promoting trust.

The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency