论文标题
道德深度学习的非歧视方法
A non-discriminatory approach to ethical deep learning
论文作者
论文摘要
人造神经网络在不断增长的任务中执行最新的工作，如今，它们被用来解决各种各样的任务。但是，典型的培训策略不考虑培训的ANN模型可能会引起的合法，道德和歧视性潜在问题。在这项工作中，我们提出了NDR，NDR是一种非歧视性正则化策略，可以防止ANN模型使用某些歧视性特征来解决目标任务，例如，像人类面孔的图像分类任务中的种族性一样。特别是，对ANN模型的一部分进行了培训，以隐藏歧视性信息，以便其余的网络专注于学习给定的学习任务。我们的实验表明，可以利用NDR来实现具有最小的计算开销和性能损失的非歧视模型。
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks. However, typical training strategies do not take into account lawful, ethical and discriminatory potential issues the trained ANN models could incur in. In this work we propose NDR, a non-discriminatory regularization strategy to prevent the ANN model to solve the target task using some discriminatory features like, for example, the ethnicity in an image classification task for human faces. In particular, a part of the ANN model is trained to hide the discriminatory information such that the rest of the network focuses in learning the given learning task. Our experiments show that NDR can be exploited to achieve non-discriminatory models with both minimal computational overhead and performance loss.
