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Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
[article]
2019
arXiv
pre-print
Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has dealt with these impossibility results by quantifying the tradeoffs between different formulations of fairness. Our work takes a different perspective on this issue. Rather than requiring all notions of fairness to (partially) hold at the same
arXiv:1902.04783v4
fatcat:ty2zftlwinatxiwzdcwkpjsryi