AI-powered translation tools like DeepL and Google Translate have revolutionized how we communicate across languages. But they’re not flawless. By looking at common translation errors, we can learn not only where AI struggles, but also how languages differ in structure, meaning, and culture.

Literal Translations

One of the most frequent mistakes is literal translation—where AI translates word-for-word instead of capturing meaning.

• Example: French “avoir faim” → “to have hunger” (literal), instead of “to be hungry.”
  These errors reveal how idioms and expressions vary across cultures and why context matters in translation.

Gender and Agreement Errors

Many AI systems trained on English struggle with gendered languages like French, Spanish, or German.

• Example: Translating “the doctor” into French often defaults to “le médecin” (masculine), even if the subject is female.
  These biases highlight the influence of training data and how societal stereotypes shape AI outputs.

Ambiguity and Polysemy

Some words have multiple meanings depending on context. AI sometimes chooses the wrong one.

• Example: English “bank” → French “banque” (financial) vs. “berge” (riverbank).
  These mistakes show how essential context is in disambiguating meaning.

Translation errors are more than just glitches—they reveal linguistic complexity and reflect biases in AI training data. Recognizing them helps us understand both the strengths and limitations of AI, and why human expertise remains vital for high-stakes translation.

Next, we’ll look at the challenges AI faces with low-resource languages and why inclusivity matters.

👉 Read next: Challenges with Low-Resource Languages