Possibly the concept of accuracy and performance metrics need a broader, more involved technique than pure numbers. It is fair to say that the data from which the Google translate learns from, is sufficiently fit into the parameters of male-female power dynamics that does exist in our society. As you can probably guess by now, the original sentence in Bengali makes no such claims of gender of the protector. In this final example, the translation embodies the gender stereotype that it is the masculine role to be a protector, shielding and caring for the feminine. Below are a few examples of this activity. When translating from our native tongue to English, it not only assigns gender to gender neutral pronouns, it does so by enforcing a large range of gender stereotypes. This distinction is, unfortunately, also carried by Google Translate. However, the contrary is often difficult, as it is almost conditioned in our English grammar to assign gendered pronouns, even while translating from a gender-ambiguous language. Thus, gendered and gender-neutral English texts can easily be translated into Bengali without loss of information, and translating texts containing gender-neutral pronouns can easily be translated into gender-neutral English. While the English language employs different gendered pronouns (‘he’ / ‘she’) and gender-neutral pronouns (‘they’ / ‘them’), Bengali uses only gender-neutral pronouns: সে (pronounced ‘shey’) ও (pronounced ‘o’), and others. Without delving into much detail about the constitution of languages, one key differentiating feature between English and Bengali is in their usages of pronouns. We will demonstrate this by employing translations into our mother tongue: Bengali/Bangla. This not only results in creating a system that is biased on a translation level, it also reinforces the very existence of it by continuing a vicious cycle of human-to-machine transfer of subjective knowledge, where the foundational conceptions of ethics and morality are lost in the mathematics. Since such systems are trained on human-validated data, it is likely that human biases and understandings of gender stereotypes are embedded deep into the systems’ workings. It aims to apply broader human contexts to translation, which is likely the cause of its encoding of biases. Google Translate utilizes a Neural Machine Translation system which aims to translate sentences as a whole, instead of their composite part, in a similar fashion to humans manually translating between languages. This process can and probably does lead to loss of important context, giving true meaning to the phrase ‘lost in translation’. words in a language A are first translated into English, and this English result is translated into language B, instead of directly translating from language A to B. Some researchers have shown that this translation process pivots through English i.e. Such crowdsourcing can lead to a variety of different human biases to become encoded into the data, and therefore into the algorithm. Training data is acquired through crowdsourcing through Google’s Translate Community, meaning that millions of people across the world contribute data for the algorithm to learn from. Therefore, these translation results were simply best educated guesses, based on mathematical decisions based on large amounts of training data. Very basically, such a system reads in large bodies of data (in this case multilingual text data from a source language to a target language), attempts to establish patterns of relationships (in this case, picking up commonly occurring source words/phrases and their translated counterparts), and then uses statistical techniques to predict an outcome (in this case, translate a given word/phrase). Google Translate began as a Statistical Machine Translation (SMT) system in 2006. In this article, we aim to explore these biases as perpetuated by Google Translate, one of the most common language translation tools used across the world from the lens of gender. The reality of natural language as source data is oftentimes in its subjective nature, and also representative of a particular country, culture, sociological and colloquial factors - and of course, its prejudices and stereotypes. Algorithmic bias and prejudice is nothing new to those who have been following the research along these lines over the past decade or so.

Stereotype brain app