Forget Privacy: You're Terrible at Targeting Anyway
Saved by Alex Dobrenko and
This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.
Saved by Alex Dobrenko and
Another big issue is that humans—and symbolic models like sets of rules and decision trees—can explain their reasoning, while neural networks are big piles of numbers that no one can understand.
But computers can also observe patterns and apply them in their decision making without having any understanding of the logic behind them. I call such an approach “mimicking.” This can be effective when the same things happen reliably over and over again and are not subject to change, such as in a game bounded by hard-and-fast rules. But in the rea
... See moreA machine-learning model, trained by data, “is by definition a tool to predict the future, given that it looks like the past. . . . That’s why it’s fundamentally the wrong tool for a lot of domains, where you’re trying to design interventions and mechanisms to change the world.”
The fact that the embeddings that emerge from this “magical” optimization process are so uncannily and discomfitingly useful as a mirror for society means that we have, in effect, added a diagnostic tool to the arsenal of social science.
both beautiful and tragic. It is beautiful because on a good day it requires very little work; you often don’t need to spend much time on the pesky job of feature engineering, and in the best case the machine takes care of a large fraction of what needs to be done. It is tragic because nothing ever guarantees that any system in the real world will
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