Be Transparent About What the Model 
Does and Doesn’t Do
The scientists and analysts using an AI tool
need to understand what it was trained on,
what kinds of inputs it handles well, and
where its confidence is lower. That
transparency isn't a weakness — it's what
allows people to use the tool appropriately
rather than over-relying on it or dismissing
it entirely. Make the model's limitations as
visible as its capabilities.
T 
 he "better the devil you know" instinct is
real — and it works in your favor if you
lean into it. When people understand how
a model behaves, where it’s reliable, and
where it isn’t, resistance drops and
confidence follows.
Successful adoption isn’t just a technical
rollout. It’s your responsibility to remove
the mystery. Here’s how to make that
transition stick. 
Ensure Outputs are Explainable
A score or classification without supporting
context will be questioned — and rightly so. If
a model can't show its work, it won't be
trusted, and if it isn't trusted, it won't be used.
Design outputs to include a confidence level,
the key factors that drove the decision, and a
clear path for the user to disagree and log that
disagreement. That override mechanism isn't
a concession — it's what makes adoption
possible in the first place.
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