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> There are so many proxies for race within the feature set. Yeah but, so what? Surely you don't believe race is a strong predictor after controlling for all t

> There are so many proxies for race within the feature set. Yeah but, so what? Surely you don't believe race is a strong predictor after controlling for all the hundred other things? Algorithms are not prejudiced and it has no reason to use racial information when so much other data is available. Even if somehow race was a strong predictor of crime in and of itself, so what? Lets say economic status correlates with race, and it uses that as a proxy. It still isn't treating a poor white person different than a poor black person. And if it makes a prediction like "poor people are twice as likely to commit a crime", well it's objectively true based on the data. Its not treating the group of poor people unfairly. They really are more likely to commit crime.

pdkl95 10y ago
> Surely you don't believe race is a strong predictor after controlling for all the hundred other things? It can be if you select the right data, algorithms, and analysis method. > Algorithms are not prejudiced That's correct. However, the selection of algorithm and input data is heavily biased. You're acting like there is some sort of formula that is automagically available for any particular social question, with unbiased and error free input data. In reality, data is often biased and a proxy for prejudice. > It still isn't treating a poor white person different than a poor black person. I suggest spending a lot more time exploring how people actually use available tools. You seem aware of how humans bring biased judgment, but you are assuming that the creation of an algorithmic tool and use of that tool in practice will somehow be free of that same human bias? Adding a complex algorithm makes it easy to hide prejudice; it doesn't do much to eliminate prejudice. > Its not treating the group of poor people unfairly. Yes, it is. The entire point of this type of tool is to create a new way we can pre-judge someone based not on their individual behavior, but on a separate group of people that happens to share an arbitrary set of attributes and behaviors. The problems of racism, sexism, and other types of prejudice don't go away when you target a more complicated set of people. You're still pre-judging people based on group association instead of treating them as an individual.
Smerity 10y ago
Algorithms _can_ be prejudiced. See [1]. Not being defined with bias or prejudice does not make them impervious to it. If a dataset is biased, as you have stated by "not trusting judges" elsewhere, then the algorithm will follow that. If the dataset contains a feature that is highly indicative (be it racial information or otherwise), your model may come to depend entirely on it. The presence or absence of other data may be irrelevant, especially if you don't perform regularization or another similar tactic. Racial (or specific feature) profiling is a very complex topic. If there's open legal questions as to whether policemen are allowed to use it, I don't feel algorithms should be allowed to. [1]: "How big data is unfair: understanding unintended sources of unfairness in data driven decision making"[1], by Moritz Hardt, a Google machine learning researcher who has published on the topic - https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de#.6divbmgwn https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de...

pdkl95 10y ago

> Surely you don't believe race is a strong predictor after controlling for all the hundred other things? It can be if you select the right data, algorithms, and analysis method. > Algorithms are not prejudiced That's correct. However, the selection of algorithm and input data is heavily biased. You're acting like there is some sort of formula that is automagically available for any particular social question, with unbiased and error free input data. In reality, data is often biased and a proxy for prejudice. > It still isn't treating a poor white person different than a poor black person. I suggest spending a lot more time exploring how people actually use available tools. You seem aware of how humans bring biased judgment, but you are assuming that the creation of an algorithmic tool and use of that tool in practice will somehow be free of that same human bias? Adding a complex algorithm makes it easy to hide prejudice; it doesn't do much to eliminate prejudice. > Its not treating the group of poor people unfairly. Yes, it is. The entire point of this type of tool is to create a new way we can pre-judge someone based not on their individual behavior, but on a separate group of people that happens to share an arbitrary set of attributes and behaviors. The problems of racism, sexism, and other types of prejudice don't go away when you target a more complicated set of people. You're still pre-judging people based on group association instead of treating them as an individual.

Smerity 10y ago

Algorithms _can_ be prejudiced. See [1]. Not being defined with bias or prejudice does not make them impervious to it. If a dataset is biased, as you have stated by "not trusting judges" elsewhere, then the algorithm will follow that. If the dataset contains a feature that is highly indicative (be it racial information or otherwise), your model may come to depend entirely on it. The presence or absence of other data may be irrelevant, especially if you don't perform regularization or another similar tactic. Racial (or specific feature) profiling is a very complex topic. If there's open legal questions as to whether policemen are allowed to use it, I don't feel algorithms should be allowed to. [1]: "How big data is unfair: understanding unintended sources of unfairness in data driven decision making"[1], by Moritz Hardt, a Google machine learning researcher who has published on the topic - https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de#.6divbmgwn https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de...