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


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Authors
Byrne, B 
Concannon, S 
Saunders, D 
Ullmann, S 
Abstract

jats:titleAbstract</jats:title>jats:pThis 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 jats:italicprior</jats:italic> 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 jats:italicafter</jats:italic> 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.</jats:p>

Description

Funder: This research was funded by the Humanities and Social Change International Foundation

Keywords
Data, Bias, Ethics, Artificial Intelligence, Machine Translation
Journal Title
Ethics and Information Technology
Conference Name
Journal ISSN
1388-1957
1572-8439
Volume Title
23
Publisher
Springer Science and Business Media LLC