It is Critical to Support Women in Technology to Prevent Society from AI Bias

It is Critical to Support Women in Technology to Prevent Society from AI Bias

Supporting women in AI has never been this critical before. A study by the World Economic Forum showed a vast gender disparity of 78 percent male versus 22 percent female in AI, and it also included data science. This discrepancy isn't just a problem in the workplace. It highlights a complex issue that transcends any particular workplace and, if left unaddressed, would have far-reaching societal consequences.

The Reality

We've seen a lot of effort to encourage girls and women to be interested in STEM fields and address digital skills gaps at a younger age than in the past. However, there is presently less effort being made to support women as they transition from higher education to a long-term career in technology. This is a difficult task for the sector. But the fundamental issue is that as AI becomes more prevalent in daily life, without a technology workforce that genuinely reflects society's structure, AI-based decisions are hampered by the designers' narrow sociological and cultural prejudices.

The impact of homogeneity in AI judgments and prejudice has already been shown in credit card and mortgage application automation, resume screening, and other areas.

The industry's problem isn't a lack of talent. According to the Turing Institute, women lag behind men in fields like computer science, data preparation and exploration, general-purpose computing, databases, big data, machine learning, statistics, and mathematics. However, most of this is because of women's confidence in proclaiming their abilities during recruiting and in the job, rather than traditional skills.


Picture: EU-Startups

Soft skills are sometimes overlooked in the IT field, where technical skills are required. Still, to go forward, a more significant focus on leadership and mentorship is necessary to create confidence and encourage a more diverse workforce. We believe stereotypes must be combated from a young age, but there is still a gap. Women in the IT industry, for example, have higher levels of formal education than their male colleagues. Still, academic citations are lower, implying a lack of confidence in sharing academic information. According to the Turing Institute, only 20% of UK data and AI researchers on Google Scholar are women. Only five of the 45 researchers with more than 10,000 citations were female.

My Personal Opinion

I speak from my experience when I suggest that women need mentors and role models. I only investigated a related job after winning a mathematics competition in university. This prompted me to start researching and reading about machine learning algorithms. The simple strategy helped me understand more about this beautiful world of technology. I struggled to study and become a programmer because I was the only woman in a room of men who were typically 10-15 years older than me whenever I entered the conference room to attend an event about technology.


Picture: CDN

There is an AI framework developed by Huawei named MindScope. This MindScope Women in Tech Community acts as a safe place for women to discuss their challenges in the workplace.

A typical case that the community received is in 2020. A young student expressed her worries about her future career in programming even if she received good grades at university. The student wanted more advanced advice from senior programmers and tech leaders. And the result of seeking more suggestions is that she is very confident after graduating and received a good offer for her job. This is an example of why we should encourage women in technology more.


However, empowering women entails more than just increasing diversity in the workplace to achieve greater gender parity. The advantages extend beyond the sector to include societal benefits. With the digitalization of many conventional industries, the widespread nature of AI necessitates that it is not just efficient but also inclusive. Only by diversifying the skill pool will we be able to avoid biased data-driven conclusions. Creating communities that actively encourage participation and various viewpoints is a crucial first step.


Picture: Techyv

In AI, bias begins with the formulation of problems. The questions are inevitably confined by the designers' and programmers' experiences. This, in turn, impacts the data's quality and how it's handled. So, what will the societal consequences be if there isn't more diversity?

  • If there isn't more input at the design stage, women's user experience (UX) will be less intuitive.

  • Economic discrimination has a long-term impact, whether it is assigning women's applications to lower-paying professions or denying them access to financial resources.

  • Whether it affects education, healthcare, or even safety, societal resources will be dispersed inequitably.

  • Women's decision-making ability for basic day-to-day decisions will deteriorate.

To summarize, now that our lives are becoming digital, we must ensure that women benefit from technology rather than being negatively impacted for future generations.

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Jessica Vieira
Jessica Vieira
Jessica Vieira is ProductReviews's senior media reporter, covering the intersection of entertainment and technology.