To address the sample selection bias between the training and test
data, previous research works focus on reweighing biased training
data to match the test data and then building classification models
on there weighed raining data. However, how to achieve fairness
in the built classification models is under-explored. In this paper,
we propose a framework for robust and fair learning under sample
selection bias. Our framework adopts there weighing estimation
approach for bias correction and the minimax robust estimation approach for achieving robustness on prediction accuracy. Moreover,
during the minimax optimization, the fairness is achieved under
the worst case, which guarantees the model’s fairness on test data.
We further develop two algorithms to handle sample selection bias
when test data is both available and unavailable.
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Sample Selection for Fair and Robust Training
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning from corrupted data, and it is known that addressing only one of them may have an adverse affect on the other. In this work, we propose a sample selection-based algorithm for fair and robust training. To this end, we formulate a combinatorial optimization problem for the unbiased selection of samples in the presence of data corruption. Observing that solving this optimization problem is strongly NP-hard, we propose a greedy algorithm that is efficient and effective in practice. Experiments show that our method obtains fairness and robustness that are better than or comparable to the state-of-the-art technique, both on synthetic and benchmark real datasets. Moreover, unlike other fair and robust training baselines, our algorithm can be used by only modifying the sampling step in batch selection without changing the training algorithm or leveraging additional clean data.
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- Award ID(s):
- 2003129
- NSF-PAR ID:
- 10311450
- Editor(s):
- Ranzato, M.; Beygelzimer, A.; Liang, P.S.; Vaughan, J.W.; Dauphin, Y.
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- Volume:
- 34
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
