Four things that can take the saving back | AI Learning Guide

Read the two columns together, because each limit has a specific way of undoing the gain. Bad data means the output has to be re-checked by hand, which is exactly the work you were trying to avoid. Bias does not slow the trial down at all — it makes the fast answer apply to fewer people, so the saving is real but the value is smaller. Privacy caps what can be automated in the first place, so it limits the size of the saving rather than erasing it. Trust is the sharpest one: if the people who have to act on the result do not accept it, the result gets re-examined, and the time you saved is spent on the re-examination. Notice that none of these is a flaw in the method itself. They come from the setting the method is used in.

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What each limit does to the saving

The limit

Data quality: records incomplete or inaccurate
Bias: historical data under-represent some groups
Privacy: record access and data use are constrained
Trust: users do not accept the output

How it takes the time back

Output must be checked and corrected, spending the saved time again
The result fits some patients poorly, so its value is narrower than it looks
Some steps cannot be automated at all, capping the achievable saving
The result is re-examined until accepted, so the speed never reaches the finish line

Why these are limits, not failures

None of these four is a defect in the technology. They are properties of the setting the technology is used in, and they set the ceiling on how much time can honestly be saved.