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

Abstract

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.

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

“…Work is underway to mitigate gender and other bias in machine learning, for example the automatic gendering discussed above, e.g. Sun et al (2019), Tomalin et al (2021). This will be especially important since automatically produced texts feed into future machine learning, potentially exacerbating their own biases.…”

Societal Biases in Language Generation: Progress and Challenges

“…The use of grammatical gender in some languages and not in others can expose unwanted gender associations (e.g., for different occupations) through translation (Prates et al, 2019). Earlier works by Vanmassenhove et al (2018) and Elaraby et al (2018) study LSTM-based encoder-decoder translation systems, and more recent works examine Transformer-based architectures (Escudé Font and Costa-jussà, 2019;Stanovsky et al, 2019;Costa-jussà and de Jorge, 2020;Stafanovičs et al, 2020;Renduchintala and Williams, 2021;Choubey et al, 2021;Tomalin et al, 2021). While Google Translate 3 has been the most popular commercial system to analyze for gender biases (Prates et al, 2019;Moryossef et al, 2019;Stanovsky et al, 2019;Farkas and Németh, 2020), Stanovsky et al (2019) Re-writing We use the term re-writing to refer to tasks of revising specific words and phrases in the original text to be more aligned with a targeted attribute.…”

Societal Biases in Language Generation: Progress and Challenges

“…The use of grammatical gender in some languages and not in others can expose unwanted gender associations (e.g., for different occupations) through translation (Prates et al, 2019). Earlier works by Vanmassenhove et al (2018) and Elaraby et al (2018) study LSTM-based encoder-decoder translation systems, and more recent works examine Transformer-based architectures (Escudé Font and Costa-jussà, 2019;Stanovsky et al, 2019;Costa-jussà and de Jorge, 2020;Stafanovičs et al, 2020;Renduchintala and Williams, 2021;Choubey et al, 2021;Tomalin et al, 2021). While Google Translate 3 has been the most popular commercial system to analyze for gender biases (Prates et al, 2019;Moryossef et al, 2019;Stanovsky et al, 2019;Farkas and Németh, 2020), Stanovsky et al (2019) 2021) also examine Yandex.…”