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 algorithm 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.

提出了一种基于样本选择的算法来进行公平和鲁棒性训练，通过解决组合优化问题来实现样本无偏选择，并通过贪心算法解决了 NP 难问题，实验结果表明该算法在合成和基准真实数据集上具有优越的公平和鲁棒性。