Sample Selection for Fair and Robust Training
This addresses the challenge of ensuring both fairness and robustness in AI models, which is crucial for Trustworthy AI, though it is incremental as it builds on existing sample selection techniques.
The paper tackles the joint problem of fairness and robustness in AI by proposing a sample selection algorithm that formulates a combinatorial optimization problem for unbiased selection in corrupted data, achieving fairness and robustness comparable to or better than state-of-the-art methods on synthetic and real datasets.
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.