The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing

• Published in: Ethics and information technology Vol. 23; no. 3; pp. 419 - 433
• Main Authors: Tomalin, Marcus, Byrne, Bill, Concannon, Shauna, Saunders, Danielle, Ullmann, Stefanie
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2021; Springer Nature B.V
• Subjects: Adaptation; Artificial intelligence; Bias; Computer Science; Datasets; Discrimination; Domains; Ethics; Gender; Human behavior; Human bias; Innovation/Technology Management; Intelligent systems; Library Science; Machine translation; Management of Computing and Information Systems; Original Paper; System effectiveness; Systems design; User Interfaces and Human Computer Interaction; Ethics; Data; Bias; Artificial Intelligence; Machine Translation
• ISSN: 1388-1957; 1572-8439
• DOI: 10.1007/s10676-021-09583-1

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.