Know the two reliance errors
Diagnose whether a team is underusing or overusing AI advice in a decision.
Two errors sit on opposite sides of the same line. Algorithm aversion is rejecting AI advice more than its actual reliability warrants. It often appears after a visible miss: "The model got that account wrong, so I do not want it in the process." The miss may matter, but the right response is to diagnose the miss and update the reliance rule. Algorithm appreciation is leaning into AI advice because it feels systematic, numeric, or less biased than humans. Appreciation can be useful when a model is validated for the task. It becomes over-reliance when the team treats confidence as…
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