The emergence of AI has sparked debates about how we evaluate and tolerate errors in machines versus those made by humans. Nowhere is this disparity clearer than in industries where safety, quality and trust are paramount.

Take self-driving cars as an example. According to a 2020 report by the National Highway Traffic Safety Administration (NHTSA), 94% of car accidents are caused by human error. Human-driven vehicles cause approximately 1.35 million fatalities globally each year, yet society largely accepts human error as an unavoidable risk. Meanwhile, self-driving cars—with significantly lower accident rates—are scrutinized far more intensely, with a single incident sparking widespread skepticism.

Humans make mistakes, and we accept this as a fact of life. Machines, on the other hand, are often expected to be infallible. The same narrative applies to the translation industry, where AI-driven solutions are judged more harshly than their human counterparts—despite evidence that human translators are far from perfect.

Double Standards In Translation

In the translation industry, accuracy is paramount. However, an internal quality benchmark from 2023 based on assessments of 1 million randomly selected human-translated words reviewed by professional linguists reveals an average of one error per 150 words. These errors range from terminology and accuracy issues to stylistic inconsistencies and linguistic errors. This means that even reputable human translators—the gold standard in the industry—introduce errors at a measurable rate.

Meanwhile, AI translation tools have reached an impressive level of precision and can process text in a fraction of the time it would take a human. Still, AI translations are frequently held to a higher bar. Many expect machine-generated translations to be flawless, even when they outperform humans in consistency and efficiency.

So, why are AI translation tools held to higher standards than human translators, and how can we bridge this gap?

The Case For Collaboration: AI And Humans

The double standard may stem from two key factors: how we attribute errors and our ability to relate to those making them. When humans err, we empathize with the effort involved and forgive them based on earned trust and shared imperfection. Machines, however, are perceived as products of design, where any error is seen as a fundamental flaw rather than a natural limitation. The belief that machines are meant to surpass human abilities further exacerbates this bias, leading to disproportionate criticism of AI—even when it consistently outperforms humans in speed and accuracy.

Rather than pitting humans against AI in a zero-sum comparison, the translation industry can achieve promising results from blending AI's strengths with human expertise. AI excels at processing large volumes of content quickly and consistently, while human reviewers provide the nuance and cultural context that machines lack. Together, they create a scalable and cost-effective approach that meets high-quality standards.

These hybrid approaches help address two critical challenges: quality at scale and consistency in tone. For instance, when multiple human translators work on a single project, maintaining a cohesive voice can be challenging. AI can help enforce consistency by flagging deviations or discrepancies, ensuring the final output aligns with the intended style and brand.

Instead of holding machines to impossibly high standards, AI can be a primary tool for delivering quality and efficiency at scale, with human expertise playing a strategic, complementary role. The current high expectations for AI can drive continued innovation, but success depends on reframing the role of human translators.

In this evolving landscape, I believe translators will become language leads—providing oversight, contextual understanding and ensuring brand alignment—while AI handles the bulk of translation work. This collaboration creates a sustainable path forward where AI’s speed and consistency, combined with human judgment and creativity, achieve realistic yet superior outcomes.

Key Takeaways For Businesses

Beyond the translation industry itself, I believe there are some important takeaways that all organizations working at the intersection of humans and machines should consider:

• Reframe perfection. Embrace the reality that no system—human or machine—is flawless. Success lies in combining their strengths to achieve optimal results.

• Adopt a collaborative mindset. View AI as a partner rather than a replacement. Leverage its speed and accuracy to complement human expertise.

• Focus on outcomes rather than origins. Whether the effort comes from humans, machines or a hybrid approach, the end goal should always be delivering the best possible quality and efficiency.

Looking Ahead

The future isn’t about humans versus machines—it’s about humans and machines working together. Just as self-driving cars have the potential to make roads safer by reducing human error, AI-driven translation tools can elevate the quality and scalability of language services. The key lies in balancing innovation with practicality, setting fair benchmarks and challenging outdated mindsets about machine performance.

The goal isn’t to eliminate errors entirely—it’s to create a world where technology and humanity complement each other in pursuit of better outcomes.

 Source: Forbes