By Dr Joss Moorkens, Dublin City University, School of Applied Language & Intercultural Studies.
There is a growing consensus on the importance of ethical training as part of university programmes. More broadly, there’s a growing interest in transversal skills. These are interdisciplinary skills that are applicable in many situations and fields and could be useful for all graduates. As part of a recent development exercise at Dublin City University, we developed a set of learning outcomes for such skills, including ethical decision-making. In the European Masters in Translation Network we are currently revisiting our Translation Competence Framework for the next round of programme applications and the inclusion of ethics is an important consideration.
In the past, ethics have mostly been applied to translation in the form of rules and guidelines for individual translators or for translation companies. In ethics, this is known as the deontological approach to ethics, and can be useful for encouraging ‘good’ decisions and to signal trustworthy and professional behaviour within the industry. However, guidelines cannot possibly cover every situation or eventuality, so we have other ethical tools and theories to help us to make better ethical decisions. The consequentialist approach, for example, leads us to consider the best result for most people, stakeholder theory is useful to think of different groups of people affected by a decision, care ethics helps us to focus on the most vulnerable, and virtue ethics provides a set of sample virtues that might help us to flourish as humans, either alone or as part of an organisation.
As the technological tools that we use every day have matured, there has been a growing awareness of ethical issues in technology. Winner (1983) wrote about how technologies engender their own worlds, and Kranzberg (1986) opined that technologies are not good or bad, but nor are they neutral. Contemporary writing on technology ethics looks at social and political contexts and interactions with power interests to identify ethical issues, often regarding machine learning or applications of artificial intelligence. Machine learning systems search for patterns in data, making predictions based on their findings. They operate by inference rather than following a direct command, their output can be opaque, biased, and unpredictable. Researchers have found issues and biases in the output from machine learning systems such as those applied to facial recognition, text creation, and machine translation.
Current machine translation systems are usually based on huge corpora of translation data, bringing to the fore issues of data ownership, data security, and data literacy. Venuti wrote about translation copyright as far back as 1998, and the Bird and Bird report (Troussel and Debussche 2014) provided great insights in the area, but ownership and control of translation data have arguably never been more important than in the neural machine translation era. The European Union has set limits on the use and reuse of personal data and made publicly funded EU translation data available globally, but the production and reuse of privately funded translation tends to be limited. Projects such as the forthcoming Common European Language Data Space will help to democratise access to data, but without the necessary computing power and technological skills, not everyone can maximise the benefits of such language data. There are lots of other ethical issues related to human and machine translation, such as risk and liability in the case of mistranslation or a data breach, computing cost and related carbon imprints, bias and misrepresentation in output, and fairness of working conditions in a part-automated translation workflow.
Translation may be considered a canary in the coalmine for the application of artificial intelligence in an industry. Repeated surveys (e.g., Pielmeier and O’Mara 2020) suggest that over 70% of professional translators work on a freelance basis, a rate higher than in many other industries, making it difficult to effectively act collectively, even within professional organisations. Retrospective analyses suggest that machine translation post-editing has represented roughly 4% of annual translation turnover in recent years (see CSA Research 2019) in a consistently growing industry, but we can probably assume that it’s available as an input for a larger proportion of translation work. The expectation is that machine translation – a key application of artificial intelligence — will make further inroads in the coming years (ELIS Research 2022). We also see new applications of artificial intelligence to translation in areas such as quality evaluation, freelance translator contracting, and predictive pricing. It is thus important that translation graduates, as future decision makers, are prepared to consider the ethical aspects and impacts of their decisions based on readings on theoretical and applied ethics alongside their translation and technical competences. Ethical decision-making is not only good for the sustainability of the industry (and our programmes), but it is also a valuable skill that can be applied in other industries and situations as graduates move through their careers in an increasingly dynamic work environment.
Some useful resources for translation and ethics include:
Kenny, Dorothy (Ed.) 2022 (forthcoming) Machine translation for everyone: empowering users in the age of artificial intelligence. Berlin: Language Science Press.
Koskinen, Kaisa and Nike K. Pokorn (Eds.) 2021. The Routledge Handbook of Translation and Ethics. Abingdon: Routledge.
Parra Escartín, Carla and Helena Moniz (Eds.) 2022 (forthcoming). Ethics and Legal Issues in Machine Translation. Berlin: Springer.
Pym, Anthony. 2012. On translator ethics: Principles for mediation between cultures. Amsterdam: John Benjamins.
CSA Research. 2019. The Largest Language Service Providers: 2019. Boston: CSA Research.
ELIS Research. 2022. European Language Industry Survey. Brussels: ELIS Research.
Kranzberg, Melvin. 1986. “Technology and History: “Kranzberg’s Laws.” Technology and Culture 27:544–560.
Pielmeier, Hélène, Paul O’Mara. 2020. The State of the Linguist Supply Chain. Boston: CSA Research. https://insights.csa-research.com/reportaction/305013106/Toc
Troussel, Jean-Christophe and Julien Debussche. 2014. Translation and Intellectual Property Rights (Report by Bird & Bird for the European Commission DG Translation). Luxembourg: Publications Office of the European Union.
Venuti, Lawrence. 1998. The Scandals of Translation. Abingdon: Routledge.
Winner, Langdon. 1983. “Technologies as Forms of Life”. In: Cohen R. S., Wartofsky M. W. (Eds.) Epistemology, Methodology and the Social Sciences. Reidel, Dordrecht, pp. 249–263.
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