Leadership in Machine Interpreting

Machine interpreting is quickly becoming a part of our reality. It is expected to see broader adoption in the years ahead, driven by significant improvements in translation quality and other factors such as economic and societal changes.

In this new era, it’s important to come to terms with a new state of affairs: spoken multilingual communication will be supported by both humans and machines. This means that translation will be performed in real communicative contexts, sometimes by humans, other times by machines—mostly operating autonomously, without human oversight. No human-in-the-loop.

Many people are finding it difficult to come to terms with this shift for various reasons. There are fears, but there are also many hopes connected with it. What is certain is that the language industry and its many stakeholders will need experts to guide them in managing this new reality. This guidance will involve deciding when and how to incorporate AI translation tools into their workflow, how to evaluate the quality of machine interpreting, choosing wisely between vendors, design certifications, and much more. What they need is leadership. And leadership requires expertise.

Expert knowledge is arising as a necessary skill to comprehend and navigate the complexity of multilingual communication when mediated by machines. Without sufficient literacy both in translation and in AI, discussions will miss the mark, false narratives will emerge, and decision-making processes will go awry. But who should be the leader here?

Currently, there is a lack of expertise and leadership in the field. When I am asked to recommend an expert, often by Language Service Providers or international organizations, I find myself without any suitable candidate to suggest. Who would I trust? Ideally, someone with substantial knowledge in both applied communication and localization industry as well as in computer science—someone who has gained this knowledge the hard way, through books, papers, conferences, and similar avenues.

Few have invested in gathering the necessary knowledge, though, and academic engagement in machine interpreting is scant. While the needed expertise is certainly not rocket science, it is not something that can be acquired on X or LinkedIn. Expertise is the result of study, inquiry, dedication, and this requires time, normally years. Creating expertise and leadership is a serious endeavor.

Interpreting Studies/Education would have been the ideal place to cultivate this knowledge which lies at the intersection between localization, IT and business management, nurturing new generations of experts capable of entering the industry as experts. But no much has come out of it, and nothing seems to be changing even now that AI is emerging with such a disruptive force. The topic of Machine Interpreting is generally not considered worth teaching or investigating. Exceptions might confirm the rule.

Conversely, in the field of Computer Science, there is a wealth of technical knowledge and a clear vision of the technological future (they are creating it), but understanding of communication in real-world contexts is rudimentary. There is often little awareness of what people expect from translation, especially in professional settings. Socio-economic considerations are largely absent among its practitioners. Here too: exceptions confirm the rule.

What now? There is no doubt that we need new leaders with a new bag of knowledge, and we need to train them. Currently, it seems no one is equipped to take on this challenge. What will likely happen is that leaders will be shaped solely through practice by the industry itself. This is a subobtimal approach, but it has worked in other cases, and it probably will work also now.

There is a positive note in this: where there is a need, there is also demand. This translates into a significant potential for young students to engage in this domain. Career opportunities for young people who have sufficient knowledge of both the world of communication and the technology of machine interpretation will be plentiful. These opportunities are likely to be lucrative, as they may involve climbing the career ladder, and very satisfying, as they require engagement with a diverse range of work scopes and challenges.

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