Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | NeurIPS | Conference |
Volume | Citations | PageRank |
abs/1809.08700 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria-Florina Balcan | 1 | 1445 | 105.01 |
| Travis B Dick | 2 | 6 | 1.93 |
| Ritesh Noothigattu | 3 | 8 | 3.97 |
| Ariel D. Procaccia | 4 | 1875 | 148.20 |