Monotonic fairness, enforced by positive-weight neural networks, avoids both class and score resentment by construction while still allowing a demographic parity trade-off.
Envy-Free Classification
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abstract
In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's. On a conceptual level, we argue that envy-freeness also provides a compelling notion of fairness for classification tasks. Our technical focus is the generalizability of envy-free classification, i.e., understanding whether a classifier that is envy free on a sample would be almost envy free with respect to the underlying distribution with high probability. Our main result establishes that a small sample is sufficient to achieve such guarantees, when the classifier in question is a mixture of deterministic classifiers that belong to a family of low Natarajan dimension.
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Avoiding Resentment Via Monotonic Fairness
Monotonic fairness, enforced by positive-weight neural networks, avoids both class and score resentment by construction while still allowing a demographic parity trade-off.