Page 143 - Proceedings of the 2018 ITU Kaleidoscope

Machine learning for a 5G future

proposed that it would be prudent to try both the A study involving computer science PhD graduates in India
approaches that were being suggested at that time. The first found that 32 percent of the graduating PhD students in
would be to choose an abstract activity like playing chess 2016 were women (Parkhi and Shroff, 2016[15]). This
and teach machines to do it. The second would be to equip figure is closer to the world average of women in science
machines with sense organs and teach them the right although it is also worth noting that a majority of the PhD
answers, like teaching a child (Turing, 1950[11]). graduates opted for teaching jobs and only a small number
went on to join research labs. Therefore the percentage of
Adam, 1998[12] uses the fascination with chess in early AI women from this pool who might have gone on to engage
works to demonstrate how the interests and worldview of in applied research is likely to be much smaller.
AI researchers influenced their conception of what
amounted to intelligent behavior. She refers to the The under-representation of women in AI research has the
following quote from Rob Wilnesky, an AI researcher, to corresponding effect of under-representation of their ideas
illustrate the point: in setting AI agendas. This imbalance also manifests itself
in other forms that go beyond issues of direct
“They were interested in intelligence, and they representation. Firstly, the few women who do manage to
needed somewhere to start. So they looked enter this field have reported systematic discrimination in
around at who the smartest people were, and terms of salaries, promotions and incidents of sexual
they were themselves, of course. They were all harassment (Vasallo et al, 2015[16]). This contributes to the
essentially mathematicians by training, and leaky pipe problem in STEM. Secondly, the AI industry is
mathematicians do two things - they prove also replete with examples of gender based stereotypes
theorems and play chess. And they said, hey, if it being reflected in the identities of AI artifacts, their
proves a theorem or plays chess, it must be functions and outputs. To some extent this can be attributed
smart.” to the lack of diverse perspectives in the designing and
testing of these artifacts.
The choice of chess and theorem proving, both being
activities predominantly associated with men, therefore For instance, virtual assistants like Apple’s Siri, Amazon’s
became a natural choice for early AI researchers (Adam, Alexa, Google’s assistant and Microsoft’s Cortana
1998[12]). The choice of chess as a metric for proving commonly come with female sounding voices (although in
machine intelligence is particularly interesting given that some cases like Apple's Siri users were later given the
the game still continues to suffer from a significant gender option to change the default voice). This is also the case
problem, resulting in the under-inclusion and under- with most GPS assistants. Several factors may contribute to
performance of women (Maass et al, 2007[13]). Yet, it this. On one hand, it could be a conscious business
would be hard to claim that that the early choices of AI decision, based on physical and psychological reasons for
researchers stemmed from any malice against women or preferring a woman's voice for such machines. On the
their role is society. Instead, these decisions reflected the other, it may be a case of unconscious reiteration of
researchers’ own experiences, interests and social society’s existing gender stereotypes -- a woman’s voice
conditioning. being regarded as more suitable for roles that demand
obedience (Glenn, 2017[17]. Similarly, the names and body
The “context” of AI researchers, which includes their shapes given to robots and other AI solutions have also
gender, has therefore defined the directions in which the been known to reflect the prevalent socio-cultural norms
field has progressed. It is possible to imagine that if the and gender identities (Bowick, 2009[18]).
group contemplating early ideas for testing machine
intelligence included some women, an entirely different set Another dimension of the gender problem in AI comes from
of ideas may have emerged. the perceptions and stereotypes of the real world, the data
that emerges from there and its use in training algorithms.
3.2 Different dimensions of gender bias This can be illustrated with a few examples. When
translation services, like the one offered by Google,
It has been over seven decades since AI first emerged as a translate text from gender neutral languages like Turkish
discipline and yet the gender imbalance in AI, and more and Finnish to a gendered one like English, the algorithm
broadly in the fields of STEM, still remains significant. As tends to attribute a gender to the subject. This classification
per data released by the UNESCO Institute for Statistics, may be based on the profession being described –
women constitute less than 29 percent of scientific engineers, doctors, soldiers are generally described as “he”
researchers globally (UNESCO, 2017[14]). Further, there while teachers, nurses and secretaries would be “she”. It
are many inter regional differences, with many developing could also relate to the activities or emotions in question –
countries showing a lower percentage of women in science. happiness and hardwork are associated with “he” while
For instance, in India's case the figure of women in science terms like lazy and unhappy with “she” (Morse, 2017[19]).
was only about 14.3 percent (UNESCO, 2017[14]).

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