Prior studies have shown that, training machine learning models via empirical loss minimization to maximize a utility metric (e.g., accuracy), might yield models that make discriminatory predictions. To alleviate this issue, we develop a new training algorithm, named BiFair, which jointly minimizes for a utility, and a fairness loss of interest. Crucially, we do so without directly modifying the training objective, e.g., by adding regularization terms. Rather, we learn a set of weights on the training dataset, such that, training on the weighted dataset ensures both good utility, and fairness. The dataset weights are learned in concurrence to the model training, which is done by solving a bilevel optimization problem using a held-out validation dataset. Overall, this approach yields models with better fairness-utility trade-offs. Particularly, we compare our algorithm with three other state-of-the-art fair training algorithms over three real-world datasets, and demonstrate that, BiFair consistently performs better, i.e., we reach to better values of a given fairness metric under same, or higher accuracy. Further, our algorithm is scalable. It is applicable both to simple models, such as logistic regression, as well as more complex models, such as deep neural networks, as evidenced by our experimental analysis.

中文翻译：

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BiFair：使用双层优化训练公平模型

先前的研究表明，通过经验损失最小化来训练机器学习模型以最大化效用指标（例如，准确性），可能会产生做出歧视性预测的模型。为了缓解这个问题，我们开发了一种名为 BiFair 的新训练算法，该算法联合最小化了一个效用和一个公平的兴趣损失。至关重要的是，我们这样做没有直接修改训练目标，例如，通过添加正则化项。相反，我们在训练数据集上学习了一组权重，这样，在加权数据集上的训练确保了良好的实用性和公平性。数据集权重与模型训练同时学习，模型训练是通过使用保留的验证数据集解决双层优化问题来完成的。总的来说，这种方法产生的模型具有更好的公平效用权衡。特别，我们在三个真实世界的数据集上将我们的算法与其他三个最先进的公平训练算法进行比较，并证明 BiFair 始终表现更好，即我们在相同或更高的情况下达到给定公平度量的更好值准确性。此外，我们的算法是可扩展的。正如我们的实验分析所证明的那样，它既适用于简单模型，如逻辑回归，也适用于更复杂的模型，如深度神经网络。