RT Article
T1 Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness
JF Journal of moral philosophy
VO 19
IS 1
SP 49
OP 78
A1 Long, Robert
LA English
PB Brill
YR 2022
UL https://www.ixtheo.de/Record/1793884900
AB As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a “fairness tradeoff” between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, philosophers have seldom examined this crucial assumption, and examined to what extent each measure actually tracks a normatively important property. This makes this inevitable statistical conflict – between calibration and false positive rate equality – an important topic for ethics. In this paper, I give an ethical framework for thinking about these measures and argue that, contrary to initial appearances, false positive rate equality is in fact morally irrelevant and does not measure fairness.
K1 statistical discrimination
K1 algorithmic bias
K1 Fairness
DO 10.1163/17455243-20213439