Although machine
translation may have brought about many positives for language service
providers and consumers alike, it is far from perfect.

There is an inherent problem
with MT that we all need to have a conversation about; the issue of gender bias
in Machine translation.

Take, for example,
the previous issue of google
translate
assigning masculine pronouns to words like; doctor
and feminine pronouns to nurse, or
assigning certain activities to specific genders when attempting to translate gender-neutral languages.

Despite the growing
awareness about gender inequality occurring worldwide, it is clear that more
action needs to take place to fix gender bias in machine translation.

This post attempts to
dissect gender bias in machine translation and the need to adopt diversity in the
corporate space.

A multidimensional issue that requires robust
solutions

The problem of gender
bias in machine translation does not reside in just the incorrect translation
of specific terms alone. It is much broader than that, and big tech companies seem
to be part of it.

A good example is the
practice of big tech companies like; Amazon, Apple, Microsoft on one hand,
assigning female names (Alexa, Siri, and
Cortana) to their voice assistant AI. While on the other hand, IBM’s
supercomputer Watson bears a
masculine, reinforcing existing gender stereotypes.

Although these companies claim that these personal assistants are
genderless,
most seem to have a female persona by default in many regions. And a good
number do it for monetary reasons since scientific studies have established
that people generally prefer the voice of a female to that of a male due to its
warmth.

As such, there is a
need for all active stakeholders to adopt a multilayered approach to solving
issues of gender bias in machine translation.

Inherent societal bias could be the source of the
problem

Making our way back
to the issue of machine translation, we all must first identify the source of
this bias. What are the root causes?

Some may argue that
it is due to inherent human bias, which AI internalizes during the
machine learning process. For example, the case of MT where bias is skewed against the
female gender since the AI segment has high male domination.

Rather, Jonathan
Davis, in his post, captures the problem succinctly. He says, “In
many cases, this does not occur as the result of active bias by machine
learning practitioners either when selecting their datasets or training their
models. Rather, inherent societal biases, such as gender or ethnicity bias,
manifest themselves in datasets which are essentially historical records of the
given society. In turn, these datasets pass on their bias to the machine
learning models that learn from them.”

But, it is common
knowledge that getting rid of existing social bias is a lot easier said than
done and it takes a long time.

Post-editing in MT plays a vital role in eradicating
bias

From the surface, it
is often easy to proffer a solution. Which, in this instance, some may say is
eliminating these biases from the datasets.

But that does not
solve the problem. These data are oftentimes a realistic representation of
events. Once excluded, could result in an incomplete dataset which can cause
large margins of error in a trained model.

One way forward is
for language service providers, who, in a bid to take advantage of what MT
offers while staying away from the scandals associated with gender bias, must
adopt post-editing procedures into their process
in order to reduce the impact of gender bias in their output to the barest
minimum.

This is not the only
solution. Stakeholders can reduce gender bias in MT by extensively training
gender-sensitive datasets and employing the most efficient machine learning
techniques to improve the accuracy of gender translation.

Perhaps, the most
effective means of eliminating gender bias from machine translation is through
the adoption of diversity in the entire ecosystem. From LSP teams to AI-based
training samples and training personnel.

This solution is a
more sustainable but long-termed approach that goes even as far as promoting
other gender participation in AI technology to balance the already
male-dominated space.

It should be worth
noting that all participants must consider data concerning all prominent and
emerging gender groups to forestall the re-emergence of this social issue.