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It's not just about bias. Even if we know there is predictive value, there's a notion of whether it's fair to include it. Regardless of what it correlates with,
by imh 11y ago
It's not just about bias. Even if we know there is predictive value, there's a notion of whether it's fair to include it. Regardless of what it correlates with, it seems unfair to make someone more/less likely to get a loan because of the color of their skin (or anything else they cannot change). There are some attributes that we should turn a blind eye to.
- nickff 11y agoSchneier's point is not only that those attributes should be ignored, but there should be no correlation between those attributes and the end result of the algorithm. You can quite easily find examples of algorithms which do not include race or gender as an input, yet have a disparate impact. Schneier is saying that algorithms with disparate impact should be avoided without regard to procedural fairness, proportionality of outcome, or correctness.
- Houshalter 11y agoWhy should fairness be a goal though? If men are really are more dangerous drivers, why shouldn't they pay more in car insurance? Or teenagers? It's not like they can control their age, but they are objectively more likely to get into an accident. So are people with eyesight problems, etc. Algorithms should make the most accurate predictions possible. If somehow we had a time machine and could just see the future, we would do that. People who are more likely to get in accidents should pay higher insurance, or bank loans, or whatever. If you are less of a risk, then you should pay less. Omitting statistically relevant features is silly. It's not like the algorithm has human biases. It doesn't hate people of a different race, it's not sexist. It just looks at the cold hard facts and makes the best prediction possible.