Summary
We’re all aware of a big push to implement AI everywhere, including in the services that many of us are working on. It seems only fair to try to give the AI some good quality input in the hope of getting decent output from it. Or, being more pessimistic: we probably expect to get some level of errors from the AI, but what do we know about the error rates in what we’re putting into the AI? In this session, we will compare our ideas on identifying errors and measuring error rates, including thinking about errors in six ways: 1) Problems along the way 2) Wrong result 3) Unnecessary action 4) Delayed-impact problem 5) Non-uptake or over-uptake 6) Technology problem We’ll wrap up with “tips and next steps”: an opportunity to consider what we now need to find out or do differently.
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