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- W2985206037 abstract "Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-learning approaches in such situations. For models that can be trained through gradient descent, we demonstrate that there are some parameter configurations that allow models to be optimized from a few number of gradient steps and with minimal data which are both fair and accurate. To learn such weight sets, we adapt the popular MAML algorithm to Fair-MAML by the inclusion of a fairness regularization term. In practice, Fair-MAML allows practitioners to train fair machine learning models from only a few examples when data from related tasks is available. We empirically exhibit the value of this technique by comparing to relevant baselines." @default.
- W2985206037 created "2019-11-22" @default.
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- W2985206037 date "2019-11-06" @default.
- W2985206037 modified "2023-09-27" @default.
- W2985206037 title "Fair Meta-Learning: Learning How to Learn Fairly" @default.
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