In this paper, we study the behaviour of the triplet loss and show that it can be exploited to limit the biases created and perpetuated by machine learning models. Our fair classifier uses the collapse of the triplet loss when its margin is greater than the maximum distance between two points in the latent space, in the case of stochastic triplet selection.

翻译：本文研究了三元组损失的行为，并表明可利用该损失来限制机器学习模型产生和延续的偏差。在随机三元组选择的情况下，我们的公平分类器利用三元组损失在其边界大于潜空间中两点间最大距离时的崩溃特性。