This article probes the practical ethical implications of AI system design by reconsidering the important topic of bias in the datasets used to train autonomous intelligent systems. The discussion draws on recent work concerning behaviour-guiding technologies, and it adopts a cautious form of technological utopianism by assuming it is potentially beneficial for society at large if AI systems are designed to be comparatively free from the biases that characterise human behaviour. However, the argument presented here critiques the common well-intentioned requirement that, in order to achieve this, all such datasets must be debiased prior to training. By focusing specifically on gender-bias in Neural Machine Translation (NMT) systems, three automated strategies for the removal of bias are considered – downsampling, upsampling, and counterfactual augmentation – and it is shown that systems trained on datasets debiased using these approaches all achieve general translation performance that is much worse than a baseline system. In addition, most of them also achieve worse performance in relation to metrics that quantify the degree of gender bias in the system outputs. By contrast, it is shown that the technique of domain adaptation can be effectively deployed to debias existing NMT systems after they have been fully trained. This enables them to produce translations that are quantitatively far less biased when analysed using gender-based metrics, but which also achieve state-of-the-art general performance. It is hoped that the discussion presented here will reinvigorate ongoing debates about how and why bias can be most effectively reduced in state-of-the-art AI systems.

本文通过重新考虑用于训练自主智能系统的数据集中的偏见这一重要主题，探讨了AI系统设计的实践伦理意义。讨论借鉴了有关行为引导技术的最新工作，并采用了谨慎的技术乌托邦主义形式，假设如果AI系统设计为相对没有表现人类行为特征的偏见，它将对整个社会都有潜在的好处。但是，此处提出的论点批评了一个良好的通用要求：为了实现这一点，必须事先对所有此类数据集进行去偏去训练。通过专门关注神经机器翻译（NMT）系统中的性别偏见，考虑了三种消除偏见的自动化策略-下采样，上采样和反事实增强-并且表明，使用这些方法对经过去偏的数据集进行训练的系统都可以实现总体翻译性能比基准系统差很多。此外，相对于量化系统输出中性别偏见程度的指标，大多数指标的性能也较差。相比之下，表明域自适应技术可以有效地部署到现有NMT系统去偏后。他们已经过充分的训练。这样一来，他们使用基于性别的指标进行分析时，所产生的翻译在数量上就不会有太多偏见，但同时也可以实现最先进的总体性能。希望这里提出的讨论将使正在进行的辩论重新焕发活力，这些辩论是关于如何以及为什么可以在最先进的AI系统中最有效地减少偏见的。