Recover trust after the AI is wrong
Respond to an AI error by recalibrating trust rather than rejecting the system wholesale.
A leader wants to remove the forecast model after one visible miss. Choose the response. Raj reacts to a visible AI forecast miss. This is why I don't want the model in our forecast process at all. fromTop Sofia needs to validate the miss without triggering blanket aversion. fromBottom The miss matters. Let's classify why it missed before we decide the model's role next cycle. Strong. It acknowledges the error and turns it into calibration evidence rather than blanket rejection. Every forecast misses sometimes, so we should keep using the model exactly as planned. This underreacts. Trust calibration requires learning…
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