Integrating artificial intelligence into the linguistic workflow is now becoming an inevitability, even though some adoption lags behind. Highly regulated industries like legal services and finance, in particular, have lagged because of a new layer of risk being introduced. But while some see AI’s autonomy as a risk, others see it as a risk-minimizer, especially when translations are fully documented.
Can AI perform in high-stakes translation?
Regulated industries are always operating under the scrutiny of national and international bodies. A single mistranslation in a clinical trial protocol or a financial disclosure could lead to legal penalties or even risks to human safety. Understandably, there is skepticism over taking shortcuts or promises of speeding up these processes – especially when the AI may not “know”, or “feel”, the sheer stakes of the work.
But, enterprise-grade translation services are being created to fill the lack of nuance that generic LLMs have. These environments are designed to automate tasks like technical manual translation or high-volume litigation discovery, all while maintaining the strict oversight required by regulators. The goal is collaboration, where AI leads the linguistic heavy lifting, but human experts still provide cultural and technical validation, much like a writer-editor relationship.
Frameworks for AI governance
Governance within AI translation is all about having a set of rules and processes that make sure the organization’s AI use remains ethical and compliant. An LLM may have full control over an internal memo translation without oversight, but a patent application needs a highly governed Human-in-the-Loop. So, effective governance begins with actually defining fitness for purpose because not every document requires the same level of AI intervention.
Sound governance is also about sticking to international standards, which are increasingly catching up to advancements in AI. For example, ISO 18587 explicitly addresses how to go about the post-edit of machine translation output. A governed workflow means companies avoid black box operation but instead has ethical guardrails and defined quality benchmarks. It’s also about predictive auditing, which is to anticipate hallucinations based on the complexity of the task. Although a simplification, the more tokens in a context window, the more that can be forgotten by the AI.
Data privacy and security
Privacy has been at the core of AI implementation hesitation, especially within regulated industries that have sensitive data. Publicly available AI models often use the user’s input to further train their future algorithms, which is an unacceptable risk for many companies, such as a law firm. Proprietary data mustn’t become public as it could breach confidentiality.
Enterprise solutions like the Seprotec AI platform have found a way around this by focusing on no-train architectures. It’s a secure environment where data is processed and translated. Anything stored will be within encrypted silos. Models are trained on external data, not user inputs.
This level of isolation is needed for GDPR compliance as it makes sure that sensitive information stays under the total control of the data owner. There is linguistic sovereignty, and it’s not just about compliance – the company’s unique terminology could grow to be a proprietary asset.
Traceability and accountability
Traceability is typically referred to as the way we track the history and location of an item – such as the farm that each bag of coffee beans comes from. In translation, this means knowing exactly which version of which AI model was used, what it reviewed and changed, and then what humans edited afterwards. Traceability is arguably improved with AI because the recording of behavior is also automated, unlike a fully human process.
Traceability is a legal requirement in industries like life sciences. If a regulator questions a specific term used in a Patient Information Leaflet, the company needs to be able to point to an audit trail of the translation process.
Advanced AI platforms can integrate metadata tracking that records every single iteration, but ultimately, regarding accountability, a human editor can still be attributed to be responsible.
A transparent paper trail also helps with feedback and improvements. Any mistakes made can be investigated and processes improved upon. Was it a flaw in the source text, a limitation of the AI engine (and if so, can it be improved or should it be accepted and acknowledged), or simply human oversight during the review?
The specific needs of regulated industries
The use of AI depends on the industry (and jurisdictions), as each is regulated differently. But also, the solutions needed vary wildly. Within law, AI is mostly for E-Discovery, which is where millions of documents are parsed quickly to find relevant evidence. Here, privacy and traceability are very important, but hallucinations aren’t all that troublesome because the AI will cite the source, which is then investigated. Often, it’s a better version “Ctrl + F”, but errors could come in the form of missing some information if there is too much context. In finance, it might be the translation of market reports. In life sciences, it’s the labeling process and clinical trial results – perhaps the largest stakes of them all.
