As the translation industry undergoes rapid technological change, the advent of artificial intelligence (AI) and machine translation (MT) has introduced both opportunities and challenges. ISO 17100:2015, the international standard that defines the quality benchmarks for translation services, has long been a guiding framework for ensuring consistent quality in translation. However, as AI-driven tools become a standard part of translation workflows, it’s worth asking: does ISO 17100 need to evolve to remain relevant?
In this article, we’ll explore how ISO 17100 compliance intersects with the rise of AI in translation, why maintaining quality in an automated landscape is critical, and what changes might be necessary to ensure that AI-enhanced translation services meet the highest quality standards.
Understanding ISO 17100:2015 and Its Importance
ISO 17100:2015 was established as a means of defining clear and consistent standards for translation services. It covers essential elements like project management, the qualifications of translators, technical resources, and the quality control processes involved in delivering professional translation services. The standard is built around the assumption of human translators handling the core of the work, with processes in place to support human quality and reliability.
With AI and machine translation now widely adopted, many translators and agencies find themselves in a unique position: leveraging technology that speeds up translation without compromising accuracy or quality. This raises a significant question about compliance: can ISO 17100 fully account for the nuances of AI in translation? And if not, what needs to change?
The Role of AI in Modern Translation
AI-driven translation tools offer various benefits, from speeding up translation times to helping translators tackle high-volume projects with complex multilingual needs. Machine translation engines, often powered by advanced neural networks, have become adept at generating initial drafts of translations, which human translators then refine in a process known as “post-editing.”
This shift has led to a new hybrid workflow model where humans and AI work in tandem. However, AI models aren’t without their limitations. They can struggle with context, cultural nuance, and idiomatic language, qualities that ISO 17100 emphasizes for professional human translators.
In this AI-assisted landscape, it’s crucial to ensure that machine-generated content aligns with the same high standards set by ISO 17100. As more agencies adopt AI as part of their workflows, the translation industry must ask how to preserve, or even enhance, quality in this new era.
How AI Challenges ISO 17100 Compliance
ISO 17100 compliance currently revolves around human translators’ skills, knowledge, and experience. The standard outlines specific qualifications for translators and revisers, ensuring that those working on translation projects have the necessary expertise to deliver accurate, culturally sensitive translations. However, when an AI engine becomes part of the process, this human-centric model is disrupted.
AI lacks the judgment and cultural sensitivity that are hallmarks of skilled human translators. An AI model can be trained on vast amounts of data, but it won’t have the professional insight to interpret cultural nuances or contextually ambiguous language. This becomes especially challenging for ISO 17100 compliance, as the current standard does not outline how to manage the quality assurance of machine-generated translations.
For instance, ISO 17100 requires a two-step review process: the translation itself and a thorough revision by a qualified reviser. When machine translation is used, this process could theoretically include a pre-editing or post-editing phase. However, ISO 17100 does not yet mandate quality control measures for MT outputs, leaving translation providers in an ambiguous position regarding compliance.
Steps Toward Integrating AI and ISO 17100:2015
To ensure that quality standards continue to apply in an AI-augmented translation environment, it may be necessary to consider updates or adaptations to ISO 17100. Here are some potential areas for improvement:
- AI-Specific Quality Control Standards: Adding guidelines specifically addressing AI-generated translations could be the next step for ISO. This could include required post-editing practices, where human translators review machine translations to ensure they meet quality benchmarks. It could also introduce a scoring system for MT outputs, allowing agencies to better assess when AI is a reliable tool and when human intervention is essential.
- Certification for Post-Editors: ISO 17100 could consider a certification standard for “MT Post-Editors,” ensuring that individuals tasked with editing machine translations have specific training in AI’s strengths and weaknesses. This role differs from traditional translation, as post-editors need to evaluate machine outputs critically and make judgment calls on cultural and contextual relevance.
- Ethical and Cultural Considerations: AI may produce linguistically accurate translations, but it still struggles with cultural sensitivity. ISO 17100 might incorporate guidelines around ethical considerations when using AI in translation, particularly regarding highly sensitive or localized content.
- Incorporating Feedback Loops for AI Improvement: Human translators can add significant value by contributing to AI’s continuous improvement. ISO 17100 could recommend structured feedback mechanisms for translators to report on AI errors, thereby refining future AI-generated translations. This would create a framework for translators to contribute to the quality of MT systems, helping the technology evolve in a way that aligns with ISO standards.
- Defining Accountability for AI Outputs: One of the most challenging aspects of using AI in translation is defining accountability for errors. ISO 17100 could introduce standards that clarify the division of responsibility between human translators, post-editors, and the AI systems themselves. Such guidelines could protect both agencies and translators by setting clear expectations for each step in the translation process.
Looking Ahead: The Future of ISO 17100 in a High-Tech World
As AI continues to redefine the translation landscape, the need for robust quality assurance practices becomes increasingly important. ISO 17100 has long been a gold standard, trusted by clients and agencies worldwide to ensure consistency and accuracy. However, for it to remain relevant in an automated world, the standard may need to evolve in a way that fully encompasses AI’s impact.
By integrating AI-specific guidelines, expanding training and certification for MT post-editors, and creating accountability frameworks, ISO 17100 can adapt to meet the needs of today’s hybrid translation workflows. As with any industry standard, it must grow alongside technological advancements to ensure that quality, ethics, and cultural sensitivity remain at the forefront of translation services.
In doing so, ISO 17100 could not only preserve its relevance but also lead the way in setting high-quality standards for AI-powered industries across the globe.
TrueLanguage invites you to learn more about our quality-first approach to translation. If you’re ready to explore how AI-enhanced workflows can still meet high-quality standards, contact TrueLanguage today and discover how we help clients navigate the complexities of modern translation with confidence and precision.
