This submission title has been editorialized, contrary to the HN guidelines. The actual article title is "Polyhedral Complex Informed Counterfactual Explanations".
Paper finds provably minimal counterfactual explanations
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Re: Paper finds provably minimal counterfactual explanations
#12In causal inference we usually call counterfactual the unobserved outcome of a treatment, and try to understand the difference in the effect of this two treatments. Here they go the other way around, they look for how the variables need to change in their classifier input so that they get the outcome that they want, and call that change an explanation, hence the name "counterfactual explanation". I don't like it but…
"Counterfactuals" in XAI aren't counterfactuals. Nor are "explanations" explanations. The whole field is basically, "you dont like these associative stats? here, what about these other ones?"
I oscillate between reading computer scientists here as liars (using this language to disguise that they cannot offer explanations, etc.), morons (who do not know the basic meaning of the terms they use) or pseudoscientists (people with no care to know, and no interest in honest communication).
In the end, the answer is perhaps much more disappointing: they're engineers with little interest in, or training around, anything beyond the most naive operational definition that suits their interest at any given moment.
However we should note how duplicitious this becomes when aligned with hype, regulatory demands, and funding models.