Re:Interpretability in Machine Learning: An Overview
by pkage on Monday November 30, 2020 @02:50PM
(#25258092)
R Guidotti et. al[0] wrote a good literature survey on black-box explainers, and contains a summary table on page 20 of the current state of the art.
In terms of designing NN-based ML, the above paper has some info and this paper by S Teso[1] is a good place to start looking further (though it is focused on XAL). SENNs are cool, but ultimately most inherenly interpretable models come down to classic ML (decision trees, linear/logreg, etc.) which is limiting compared to the power of NNs. Post-hoc explanations are basically the only option (esp. for DNNs).
In terms of designing NN-based ML, the above paper has some info and this paper by S Teso[1] is a good place to start looking further (though it is focused on XAL). SENNs are cool, but ultimately most inherenly interpretable models come down to classic ML (decision trees, linear/logreg, etc.) which is limiting compared to the power of NNs. Post-hoc explanations are basically the only option (esp. for DNNs).
[0] http://arxiv.org/abs/1802.01933
[1] https://www.semanticscholar.org/paper/Toward-Faithful-Explan...