Alternative Perspective: Why AI Translation Should Be Evaluated Through Risk Mitigation, Not Just Quality Scores

When organizations evaluate AI-assisted language translation workflows, the conversation often gravitates toward quality: how close is the output to human-level fluency? Can it pass as “native”? Does it score high on BLEU or MQM metrics? Essentially, how can we integrate AI into our translation workflow while still maintaining an acceptable level of quality.

While this is a valid question, it potentially misses the bigger picture. In real-world business contexts—whether in life sciences, aerospace, legal, or market research—the true consideration in evaluating AI translation translation workflows may be in risk management. Translation quality isn’t just about polish; it’s also about avoiding costly errors, compliance breaches, and reputational harm. Let’s take a look at how the perspective of Risk Management in translation workflows differs from Quality Management, and how that might be a better perspective when evaluating AI translation integration for your content. 

The Difficulty in Relying (solely) on Quality Measurement in Translation

AI translation systems are advancing rapidly, but no system—human or machine—produces flawless output every time. Quality is subjective, context-specific, and often debated among linguists. A sentence that looks “accurate” in a general context may be dangerously misleading in a regulatory, technical, or medical setting.

Focusing solely on quality scores may create a false sense of security. A 95% accurate translation might be acceptable for an internal email, but disastrous in an instruction-for-use document for a medical device. What matters isn’t the numeric quality score—it’s the potential for risk in mistranslation for your specific content. Is even a single error acceptable in your translation project? What is the potential impact of a single error in a life science translation, in your market research data collection, or in your product safety manual?

With translation quality assessment being a potentially shifting target, where does that leave us in assessing AI translation workflows? Can you rely on pure AI translation for your content, or would it make more sense to create a workflow that includes professional human linguists, and potentially incorporating additional translation assets like translation style guides, translation memory, and glossaries.

Risk Over Quality: A Different Perspective

When you shift the focus from quality levels to risk mitigation, AI translation workflows become easier to evaluate. Here’s why:

1. Context Matters More Than Scores
   

   • A mistranslation in a marketing tagline carries reputational risk.
     

   • A mistranslation in a safety manual carries legal and human safety risk. By framing AI translation around risk, you prioritize safeguards where consequences are highest.
     

2. Workflows Can Be Designed by Risk Tier
   

   • Low-risk content: AI output with minimal review.
     

   • Medium-risk content: AI output + targeted human editing.
     

   • High-risk content: AI only as a productivity tool, but with full human translation/validation. This creates efficiency without compromising compliance.
     

   • A note on translation assets: Assets such as translation style guides, translation memories, and glossaries can be employed into any workflow, but will increase cost through the creation of the assets and the effort required on the translator’s part to reference them. This may negate any desired goals around cost or time savings. Another quality / risk balancing act.
     

3. Clients and Regulators Care About Liability
   

   • Regulators don’t ask if your translation scored 90/100 on BLEU. They ask: “Did your process prevent errors that could harm patients, customers, or users?” Risk-based processes may be easier to defend in audits than quality metrics. This may also provide a more relatable metric to functions that are unfamiliar with the translation process.

Case Example: Life Sciences

In life science translation, a mistranslated dosage instruction can have life-or-death implications. AI may speed up translation, but without human oversight, the risks are too great. Instead of asking “Is the AI’s translation good enough?”, the smarter question is:

“What’s the risk if this translation is wrong, and how are we mitigating it?” That mindset shifts the workflow from “chasing perfection” to “ensuring safety.”

Conclusion: Risk May be the Benchmark to Consider Over “Quality”

AI translation should be judged not by whether it can achieve human-like quality in every case, but by whether organizations can use it without introducing unacceptable risk. Quality is a moving target; risk is measurable and defensible.

By reframing evaluation in terms of risk mitigation, companies can:

• Deploy AI translation more confidently.
  

• Align processes with compliance and audit requirements.
  

• Balance speed, cost, and safety across different content types.
  

In short: AI translation success isn’t about perfection—it’s about protection. If you’re interested in learning more about AI-Assisted Human Translation workflows you can read more about our offering here: https://www.languageintelligence.com/ai-assisted-human-translation. We’re always happy to discuss AI integration and translation risk and quality metrics as it relates to your specific project work. Feel free to reach out for a conversation!