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