In the context of machine translation—and language AI more broadly—the term bias refers to the tendency of a system to make the same assumptions repeatedly. At first, it might seem that there’s little overlap. AI systems are ostensibly built with neutral algorithms that don’t carry human biases. But algorithms aren’t the only factor that these systems are built on, and in practice, human biases can and do creep into them inevitably.
But what kind of biases can machine translation have? It’s not as though MT has a mind of its own, like humans, that are capable of carrying prejudices, whether conscious or unconscious. But what machine translation has is a tendency to magnify human assumptions about language, both the good and the bad.
While MT algorithms can be considered technically neutral and south africa mobile database objective, the data on which language models are trained are not.
It’s a matter of statistics, first of all. Massive amounts of linguistic data, all generated by humans, is needed for language models to work. Without extensive intervention, that data will naturally reflect the biases in human thought and speech that already exist, and replicate it in whatever output is made.
MT systems make choices based on statistics
In the case of machine translation, the AI will tend to make assumptions about translation output that is statistically more likely to occur based on examples from the data provided.
This can have more innocuous results—for example, in translating the word lift into Spanish. In Spanish, this can translate to levantar and elevar, which can be considered synonyms. But levantar is the more commonly used, so most machine translations will choose it in translating text.