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Title: Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on similar but slightly different tasks within a given domain. The second is a fair meta-learning approach to train models that can be quickly fine-tuned to specific tasks from only a few number of sample instances while balancing fairness and accuracy. We demonstrate experimentally the individual utility of each model using relevant baselines and provide the first experiment to our knowledge of K-shot fairness, i.e. training a fair model on a new task with only K data points. Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.  more » « less
Award ID(s):
1633387
NSF-PAR ID:
10183993
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Page Range / eLocation ID:
200–209
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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