Re:Interpretability in Machine Learning: An Overview
by taeric on Monday November 30, 2020 @12:17AM
(#25250946)
This is the type of hypothetical that kills the discussion, though.
If the model is interpretable, you have a high chance of knowing why it does or does not tell a stop sign from a jacket. If it is not, you only know that in your test/validation set, it can do the job.
Even tasks that machine learning clearly excels at is currently in a state where all good uses of it has a human supervisor at some level. Recognizing faces, as an example. For my personal library, I absolutely have to disambiguate the recognized faces of my kids as they get older in all of the products I've used.
If the model is interpretable, you have a high chance of knowing why it does or does not tell a stop sign from a jacket. If it is not, you only know that in your test/validation set, it can do the job.
Even tasks that machine learning clearly excels at is currently in a state where all good uses of it has a human supervisor at some level. Recognizing faces, as an example. For my personal library, I absolutely have to disambiguate the recognized faces of my kids as they get older in all of the products I've used.