  Human trust is critical for trustworthy AI adoption. Trust is commonly
understood as an attitude, but we cannot accurately measure this, nor manage
it. We conflate trust in the overall system, ML, and ML's component parts; so
most users do not understand the leap of faith they take when they trust ML.
Current efforts to build trust explain ML's process, which can be hard for
non-ML experts to comprehend because it is complex, and explanations are
unrelated to their own (unarticulated) mental models. We propose an innovative
way of directly building intrinsic trust in ML, by discerning and measuring the
Leap of Faith (LoF) taken when a user trusts ML. Our LoF matrix identifies
where an ML model aligns to a user's own mental model. This match is rigorously
yet practically identified by feeding the user's data and objective function
both into an ML model and an expert-validated rules-based AI model, a verified
point of reference that can be tested a priori against a user's own mental
model. The LoF matrix visually contrasts the models' outputs, so the remaining
ML-reasoning leap of faith can be discerned. Our proposed trust metrics measure
for the first time whether users demonstrate trust through their actions, and
we link deserved trust to outcomes. Our contribution is significant because it
enables empirical assessment and management of ML trust drivers, to support
trustworthy ML adoption. Our approach is illustrated with a long-term
high-stakes field study: a 3-month pilot of a sleep-improvement system with
embedded AI.
