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In the past couple of years, research into systems that produce increasingly sophisticated representations of words has come into its own, with research groups releasing more and more powerful models that consistently beat the then-state-of-the-art system.

Bias in the machine learning sense is defined a bit differently, as an “error from erroneous assumptions in a learning algorithm.” In other words, a model consistently making the same mistakes.

The pre-existing biases in our society affect the way we speak and what we speak about, which in turn translates into what’s written down, which is ultimately what we use to train machine learning systems.

In case you missed our article on how machines understand language, the short version is that words are represented by lists of numbers called word embeddings that encode information about the word’s meaning, usage, and other properties.

The training data used in the language model that produced the analogy most likely included men programming in the same linguistic context as women homemaking a lot more often than women doing anything else.

however, if nothing else, we also need to keep in mind that biased models are not just producing gauche analogies — sometimes they’re straight-up inaccurate: a female computer programmer is not equivalent to a homemaker.

One such technique is “gender-swapping,” where the training data is augmented such that for every gendered sentence, an additional sentence is created, replacing pronouns and gendered words with those of the opposite gender, and substituting names with entity placeholders.

However, it is important to note that this approach is straightforward for English, a language without productive grammatical gender, whereas for many other languages merely swapping pronouns like his/her and nouns like sister/brother is not sufficient, because adjectives and other modifiers express gender as well.

As Helena Moniz, a linguist and researcher at the University of Lisbon explained, “languages derived from Latin lost their neutral grammatical gender a long time ago.” To my knowledge, research into this kind of de-biasing technique for non-English corpora is largely unexplored.

If the Hungarian-English system was trained in this way, we could ask it to translate “ Ő egy orvos” and receive the translation “She is a doctor,” or “ Ő egy nővér” and receive “He is a nurse.” To perform this at scale, we would need to train an additional model that classifies the gender of a sentence and use it to tag the sentences, adding a layer of complexity.

We’re still struggling with a lack of diversity in the AI industry — according to MIT Technology Review, “women account for only 18% of authors at leading AI conferences, 20% of AI professorships, and 15% and 10% of research staff at Facebook and Google, respectively” — and we can’t deny that this isn’t partially responsible for the problem.

Especially since the consequences of our inaction aren’t just anecdotal, like the examples we’ve shared — bias in algorithms can lead to discrimination in hiring processes, loan applications, and even in the criminal justice system.

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