We introduce a new family of fairness definitions that interpolate between statistical and individual notions of fairness, obtaining some of the best properties of each. We show that checking whether these notions are satisfied is computationally hard in the worst case, but give practical oracle-efficient algorithms for learning subject to these constraints, and confirm our findings with experiments.
|Original language||English (US)|
|Title of host publication||35th International Conference on Machine Learning, ICML 2018|
|Editors||Jennifer Dy, Andreas Krause|
|Publisher||International Machine Learning Society (IMLS)|
|Number of pages||9|
|State||Published - 2018|
|Event||35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden|
Duration: Jul 10 2018 → Jul 15 2018
|Name||35th International Conference on Machine Learning, ICML 2018|
|Other||35th International Conference on Machine Learning, ICML 2018|
|Period||7/10/18 → 7/15/18|
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