Okay, this article comes at the perfect time! It's super interesting how you highlight the need to interpret system behavior now, not just user. How do we even begin to measure that 'earning trust' bit, tho?
Great question. I don’t think “earning trust” shows up as a single metric, and that’s part of the challenge. In practice, I look for patterns over time rather than scores at a moment in time.
A few signals I’ve found useful: how often recommendations are overridden and why, whether users can accurately predict what the system will do next, and how trust shifts after edge cases or errors. Longitudinal studies are especially telling, because trust calibration changes as the system adapts and as users learn its limits.
So it’s less about measuring trust directly and more about observing whether trust is appropriately calibrated for the task and risk involved. When those signals drift, that’s usually where investigation is most valuable.
Okay, this article comes at the perfect time! It's super interesting how you highlight the need to interpret system behavior now, not just user. How do we even begin to measure that 'earning trust' bit, tho?
Great question. I don’t think “earning trust” shows up as a single metric, and that’s part of the challenge. In practice, I look for patterns over time rather than scores at a moment in time.
A few signals I’ve found useful: how often recommendations are overridden and why, whether users can accurately predict what the system will do next, and how trust shifts after edge cases or errors. Longitudinal studies are especially telling, because trust calibration changes as the system adapts and as users learn its limits.
So it’s less about measuring trust directly and more about observing whether trust is appropriately calibrated for the task and risk involved. When those signals drift, that’s usually where investigation is most valuable.