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<\/p>\n\nLet\u2019s be honest, no matter how good your foreign language skills are, every now and then it really helps to type a sentence into a translation tool like DeepL or Google Translate and get a machine translation, right? The problem: if you\u2019re not well versed in the respective foreign language, you probably won\u2019t notice if your AI tool delivers a result that would make any native speaker\u2019s hair stand on end. One example is the much-discussed topic of \u201cgender.\u201d This clearly demonstrate how machine translation solutions can be useful in some cases, but exceedingly susceptible to error in others.<\/p>\n\n
So are concepts like deep learning, machine learning or AI testosterone-driven macho systems per se? Or why else don\u2019t these tools suggest females terms like \u201c\u00c4rztin\u201d or \u201cKFZ-Mechanikerin\u201d? The answer is complex. For one thing, the field of artificial intelligence is dominated by white, male scientists. According to the MIT Technology Review, only 18 percent of the speakers invited to leading AI conferences are women, while at Facebook and Google only 20 percent of AI professors and ten percent of research assistants are female. But that is only one small aspect of a larger problem.<\/p>\n\n
Machine learning systems certainly can be powerful tools, but they are only as good as the data fed into them. In other words, if there is a systematic error in the data used to train a machine learning algorithm, the resulting model will reflect this.<\/p>\n\n
In most cases, this is not just a matter of bias or stereotyping. It is also not down to those people, who may have been superficial in selecting their data sets or training their models. No \u2013 inherent social distortions manifest in the results of machine translations, much like historical records of a society merely provide highlights of the overall situation. Datasets, in turn, pass on their bias \u2013 or superficiality \u2013 to the machine learning models that learn from them. If more men than women had been physicians in the past, any machine learning model trained on historical data would \u2018learn\u2019 that physicians are more likely to be male than female, regardless of the current gender distribution among physicians.<\/p>\n\n
Machine translation models are trained using huge textual contexts. They include pairs of sentences that have already been translated once before. Added to this are linguistic nuances that often make it difficult to provide an accurate and direct translation. When translating from English into languages such as German, French, or Spanish, gender-neutral nouns are translated into gender-specific nouns. Although the word \u201cfriend\u201d is gender-neutral in English, the term becomes \u201camiga\u201d (feminine) or \u201camigo\u201d (masculine) in Spanish. A human translator can ask: are we talking about a man or a woman? The machine translation tool is not in a position to ask for this context.<\/p>\n\n
Things get really complicated when machine tools are used to translate speeches and the like. Spoken language can contain intonations, facial expressions, repetition, sarcasm, or irony that can completely change the meaning of a sentence. Modern machine translation solutions do not accurately reflect such nuances. There are other examples that show the limitations of translation tools. For example, if you type \u201ccream of tartar\u201d (a type of baking powder) into Google Translate, you get the literal German translation \u201cSahne von Zahnstein.\u201d In the other direction: \u201cKernseife\u201d used to end up as \u201cnuclear soap\u201d a few years ago. But in this particular example, the system has learned in the meantime and now translates it as \u201ccurd soap.\u201d<\/p>\n\n
Translation errors like this can be explained by the fact that Google Translate worked for many years with statistical translation methods that proceeded word by word and did not recognize correlations. Fails like this are far rarer today than they were a few years ago. Google Translate now relies on neural networks, i.e., artificial intelligence, to more accurately capture context for many of the more than 100 languages it has on offer. Scientists are also working flat out on the gender challenge.<\/p>\n\n
There\u2019s no doubt about it! Machines work really well with structured language for specific applications. Examples include weather reports, financial reports, government minutes, or sports scores. However, there are many areas where these smart tools quickly reach their limits and human translators and language experts have to get involved. Anyone who has ever tried to translate a snappy marketing text by machine will quickly see that it simply doesn\u2019t work! Thus, many customers of the perfume chain Douglas in Germany misunderstood their slogan \u201cCome in and find out\u201d to mean \u201cCome in and find your way out again.\u201d Legal texts should also be translated by experts rather than machines and then carefully proofread again to avoid misunderstandings \u2013 or even lawsuits.<\/p>\n\n
Even though machine translation tools have become better and more accurate in recent years, and many studies have addressed gender-specific challenges in this regard, there is still a huge amount of catching up to do in this area \u2013 as there is in the entire field of gender research and equality. So it\u2019s a good thing that human translators have the necessary linguistic skills and intuition to help out. And there\u2019s nothing wrong with good interaction between humankind and machine, is there?<\/p>\n\n