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"If the test results show that the algorithm under question can distinguish attributes—like whether a data point represents a male or female—then it’s labeled b
by Malician 11y ago
"If the test results show that the algorithm under question can distinguish attributes—like whether a data point represents a male or female—then it’s labeled biased. The other algorithm the researchers introduced tries to remedy the bias by modifying the actual data set so that any selection algorithm would deliver fair results. The algorithm does this by blurring attributes that may be correlated to, say, race or gender."
So if you're testing construction workers, you identify bias, then you select strength as the variable causing bias, and you adjust the strength values of the dataset or force the algorithm to devalue strength?
Racism/sexism are real and we need new ways to fight them, but this is like a right wing satire of affirmative action.
- 0xcde4c3db 11y agoI think the desirability of eliminating this kind of bias depends on the context of the problem that the algorithm would be used to solve. Per your example, suppose that strength is what an algorithm is predicting. We might not want the algorithm to lowball the strength of a candidate just because that candidate "looks like a woman" due to some other causally unrelated correlation.
- Malician 11y agoSure, and I agree. But that's not what they're doing here. Paper is available online: http://arxiv.org/pdf/1412.3756v3.pdf http://arxiv.org/pdf/1412.3756v3.pdf