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That's ok, but based on your example, how would a human apply an algorithm that it can't determine why it worked? We're not talking about "Not Hotdog" here. Ap
by Reebz 6y ago
That's ok, but based on your example, how would a human apply an algorithm that it can't determine why it worked?
We're not talking about "Not Hotdog" here. Application of a model in a real-world at-scale scenario is a lot more than running inference and walking away.
At a bank credit decisions are evaluated by humans, frequently and often. These reviews are conducted in the forms of sampling audits, control processes, and other scenarios that involve internal bank employees and external regulators. In each case, humans will inspect the details of what occurred. This would be impossible with any type black-box model (SSL, deep NN, etc.).
- elcomet 6y agoI'm not sure I understand your comment. Bank credits are auditable, because there are some precise algorithm around them. Humans created the algorithm specifically so that it is interpretable. Then they must apply it the same way to everybody. They can't just say "oh, I think this person is more likely to actually reimburse, I play golf with him and I trust him !" This is a specific case where no black box will ever be used (at least I hope).
- loopz 6y agoThere's incentive to use complex black boxes everywhere, if more profitable.
- elcomet 6y agoOf course, and my point is that it's perfectly fine, and even desirable, as better models lead to better outcomes. Of course, for cases where explainability is needed, either required by law (such as banking), or by common sense, then black boxes will not be deployed.