A Survey Of Methods For Explaining Black Box Models — mentioned by pkage

Mon, 30 Nov 2020 19:50:33 GMT

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). [0] http://arxiv.org/abs/1802.01933…

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A Survey Of Methods For Explaining Black Box Models — mentioned by pkage