Quite interesting development. I had to talk the nature and implications of What a 'black box' (BB) . was for management many times. A BB is simply a method that is not precisely know in its operation. Usually such an algorithm's method IS known to the person or AI that developed it, but is obscure to the people who need its operation in context. . It may be 'explainable', but it has never been sufficiently explained to the user who wants or needs it. . It may be the explanation is too difficult. It may require considerable math, statistics or AI. Or, and not uncommon, the user may not have asked for an explanation. Perhaps because they liked the outcome of the BB. Lots of practical subtilties here. Measuring understanding is a useful step.
By MIT News, May 11, 2022
A mathematical framework developed by researchers at the Massachusetts Institute of Technology and Microsoft Research aims to quantify and evaluate the understandability of a machine learning model's explanations for its predictions.
The framework, called ExSum (explanation summary), can evaluate a rule on an entire dataset. ExSum enables the user to see if a rule holds up based on three metrics: coverage, or how broadly applicable the rule is across the entire dataset; validity, or the percentage of individual examples that agree with the rule; and sharpness, or how precise the rule is.
Said MIT's Yilun Zhou, "Before this work, if you have a correct local explanation, you are done. You have achieved the holy grail of explaining your model. We are proposing this additional dimension of making sure these explanations are understandable." ...
Researchers have created a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them....
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