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by dogruck
3205 days ago
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I agree with everything you said, and your experiences match my own. I still say that situation leads to formulaic, systematic management. Let's say that GOOG developed a sophisticated machine learning algorithm that made all hiring, firing, promotion, and compensation decisions. The code is open source, and everyone can see that it doesn't contain explicit logic for bias. Now, a short, 44 year old, male engineer sues because of a statistically significant observed bias against one of his cohorts. Is the program biased, or just insightful? With humans, it seems we have no choice but to assume their collective algo is biased. And it often is! But, when you're a massive monopoly with tons of cash, the safest thing is to make formulaic decisions that are statistically clean. It's just good business. |
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We've seen this exact thing with other machine learning algorithms - there was the one in the news recently that insisted on classifying a photo of a man in the kitchen as a woman, because all its training data firmly convinced it that women are the only people in the kitchen.
I guess the thing worth explicitly asking is whether biases for entirely logical reasons are defensible - for instance, if you start with an industry where (for whatever reason) men are paid much more highly than women, it's okay to offer people their current salary + fixed delta to poach them from their jobs. I would say no, because the goal of legal policies like this is to achieve a specific result in society, and specifically not to punish bad people in charge of companies, so the question is not whether people had a bad motive, but whether the result is being achieved. If you're allowed to apply a non-biased algorithm to biased starting data and have it yield an equally-biased output, you're not actually solving the bias.