As technology becomes more widespread, more and more tech companies are beginning to recognise the importance of diversity within their organisations. Although women are still largely the minority when it comes to science, tech, engineering and math (STEM) fields, the situation isn’t as dire as it used to be. 

According to latest figures measuring the percentage of women in the biggest tech companies, the number of women in tech corresponds to the number of women in the workforce within other industries. 

This indicates that the lack of gender diversity is not just an issue in the tech industry. The good news is that people are now more aware of it, leading to them taking action to improve the situation.

Over the years, tech companies like Facebook have seen a growing percentage of women in their workforce, with significant increases in technical and leadership roles. In 2018, 36% of Facebook employees were women. 

With the way work is changing, there are more opportunities for women to work the way they want to. 

The rise of the gig economy has given stay-at-home moms and other women who can’t commit to the nine-to-five an opportunity to join the workforce on their own terms. This new culture is slowly but surely evening out the scales that is gender diversity. 

At the same time, there is a growing number of prominent women in tech who are paving the way for other women and adding their voice to the calls for diversity. They’ve taken it upon themselves to show the world that diversity is not just necessary; it’s critical to success.  

Preventing gender bias in machine learning

As critical as diversity is in any industry, it is even more so in artificial intelligence and machine learning. Many AI companies are beginning to realize that it’s necessary to have a diverse workforce, especially since case studies have shown that AI systems have fallen short with female users or even shown discrimination based on culture.  

Inherent bias while developing AI/ML models and algorithms can result in machines that exclude certain portions of the population. Considering how diverse the world really is, this bias results in machines that do not perform as well and could even be considered “faulty”.

In 2016, a researcher found that YouTube’s auto captions performed much better on male voices compared to female voices. This wasn’t an isolated case either; it had been a long running problem since the emergence of dictation software.

It’s not that women’s voices are more difficult to capture than men’s. They’re just different, which means that if a system is designed with men’s voices as its base, it just doesn’t work as well for women. 

If a tool doesn’t work for half the population, it’s a tool that doesn’t work. Full stop. 

In fact, there have been situations where gender bias has led to incorrect data analysis. Image recognition software has incorrectly labeled pictures of people in the kitchen as “woman”. It may seem like a small issue, but imagine if a system was designed to be used for safety or life-saving. The implications are much more severe.   

In order to produce machines that are truly effective and can function at higher levels of accuracy, it’s crucial to increase diversity in the AI development phase. If removing gender bias is the goal, women have to be included in the process. 

woman sitting on chair

Hidden figures in the machine

In the AI/ML industry, there’s already a portion where women are making up a significant percentage of the labor pool — data labeling. 

Data labeling used to be seen as “low level work”, akin to rote labor on factory floors. This part of the AI/ML process was usually outsourced to lower income countries, where workers were overworked and underpaid. 

However, companies are beginning to see the need for a specialised workforce. Data labelers today have to be skilled enough to provide the “human touch”, imbuing the algorithm with enough nuance and ability to make accurate judgements. 

Although the work is labor intensive, it’s also something that can be done remotely, without fixed working hours. The data labeling process also requires a high level of attention to detail, which many women excel at. 

This makes it the perfect fit for a lot of women, especially stay-at-home moms who are often skilled workers who want to do more than just housework and childcare. Creating the right environment for work means being able to harness all their skills and knowledge that would otherwise be lost.

The high percentage of women in the AI/ML industry has shown that it’s possible for women to take on more prominent roles in tech. And if we want the tech industry to continue to advance, it’s necessary to develop the right conditions that make the working environment inclusive. 

When it comes to diversity in the workplace, we still have a long way to go. It’s a topic that needs its proponents. We must continue to keep discussing it and create an awareness of its importance. 

And as more women rise up the ranks in tech, they will hopefully, inspire the next generation of data scientists, engineers and CTOs. 

Gender equality is not something that happens overnight and although we may not be there yet, we’re making progress.  

Contributed by Susian Yeap, COO & Co-founder of Supahands

About the author

Susian Yeap is CO-founder and COO at Supahands. She leads talent and people operations and ensures all members of the SupaTeam have a great, and impactful experience at the organization. She also oversees business operations at Supahands and looks after all processes and operational aspects of the organization.

Susian has a deep-rooted passion for people and strives to create communities and environments that foster talent and drive growth. At Supahands, Susian builds a balanced and dynamic team that can navigate the fast-paced technology industry and innovate to stay ahead. She believes in ‘getting her hands dirty’ when setting up processes to thoroughly understand the issues the SupaTeam is facing and how to address them correctly and effectively.

Prior to her career at Supahands, Susian worked for Accenture where she managed mergers and acquisitions. She brings with her extensive experience in consulting and project management in the financial services sector, as well as system implementation – which included moving entire organizations from legacy systems to digital platforms.

Outside of the office, Susian is passionate about health and fitness. She spends her free time at the gym and likes to travel and dive. When at home, she enjoys baking, spending time with her friends, family, and Bam Bam the pug.