A few decades ago, movies about robots taking over the world flooded our cinema screens. The likes of The Matrix trilogy and I, Robot depicted a world in which technology had superseded human intelligence and developed beyond our control.
Today’s reality isn’t so much artificial intelligence (AI) ‘taking over’, but the threat of it ‘being wrong’. Or rather, being subjective. Machines are able to ‘learn’ the way that humans learn, but when they’re programmed by biased people, they’ll learn biased responses. That’s why we need more diversity in the field of Machine Learning.
Encouraging women and the BIPOC (black, indigenous, people of color) community into the field of Machine Learning needs to be on the agenda. And we need to do it sooner rather than later.
What exactly is Machine Learning?
Machine learning is a branch of AI and computer science. Through the use of data and algorithms, it imitates learning the way that humans learn, getting more accurate with continued use.
Take a few examples:
- Speech recognition technology like Siri – it learns the sound of your voice and responds to questions according to your unique preferences.
- Chatbots and virtual assistants – they remember past conversations and provide information based on how you respond to questions.
- Recommendation engines like Spotify or Netflix – they personalize and recommend new content based on your preferences and what you’ve watched/listened to in the past.
Knowledge of data science is valued in many global organizations today as they build intelligent systems based on Machine Learning. However, organizations need to be aware of the implications of not hiring diversely in this space. It could have a serious impact in future.
To put it simply: We need more women and BIPOC leading the way in Machine Learning.
Why are there so few women and BIPOC in Machine Learning?
There aren’t enough in tech, period
The tech industry in general is male-dominated. McKinsey research shows that 37% of entry-level tech jobs are filled by women, but this number drops significantly for senior management roles (25%) and executive roles (15%). Not only do we need to focus on drawing more diverse people into tech, we need to give them leadership opportunities.
There are barriers to entry
Despite progress in workplace equality, women and minority groups are still losing out. A gender pay gap, unequal parenting requirements and non-inclusive workplace language and treatment deters many women from the field of Machine Learning. The same goes for BIPOC. It makes sense that they fall back on other industries that are easier to enter.
There’s a lack of support
Girls in Tech exists because our founder Adriana Gascoigne discovered a lack of support for young women in tech. While there are some excellent organisations advocating for women in tech now, there are still more men than women in tech.
It’s not that there aren’t women in the Machine Learning space at all. There are some amazing women doing fascinating things (check out our write-up on Inioluwa Deborah Raji and Timnit Gebru), but there’s still a lack of role models for women to aspire to.
Why is diversity in Machine Learning so important?
Our differences are the very thing that make us gloriously human. This is why Machine Learning needs input from people of all backgrounds. With only 13.5% of the field made up of women, data is being gender-skewed. And it’s a similar story for other minorities.
Welcome to the core issue of Machine Learning: it’s becoming more prevalent in our daily life, with the World Economic forum anticipating 97 million new jobs in the sector by 2025 – yet the field is dominated and influenced predominantly by white men. This won’t result in a fair and equitable future, considering how much Machine learning will be in it.
Fei-Fei Li, Sequoia Professor of Computer Science at Stanford University, said:
“If we don’t get women and people of colour at the table — real technologists doing the real work — we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible. This is the time to get women and diverse voices in so that we build it properly.”
Diversity in AI is a problem we need to tackle now, before it becomes too hard to undo.
So what can you do about it? Speak about this issue, celebrate and uplift the efforts of women in AI, or maybe work in the industry yourself.
Tips for breaking into Machine Learning
Harness the power of data
Lynne Bishop, Head of Analytics at edX, shared that “relying on data, strong analyses, and clearly articulating the connection between the numbers and what they meant always gave me an edge in a discussion”. Her advice is to understand the power of data, and use this knowledge to drive your career in Machine Learning.
Share your knowledge
Data analysis digs up the most amazing insights. So share these with the world! Whether you share your findings internally by presenting to your company, or externally by speaking at events or releasing articles, take credit for your findings and make them known.
Get a mentor
Connecting with other working women can offer a leg up in your career. It may be hard to find someone in your field, but don’t be afraid to look to other industries for the right mentor to help you navigate your career.
Find the right companies
Machine Learning is still a growing field, and many companies are yet to develop structured roles and systems around it. Look for opportunities to learn from top companies who are leading the way in data. The Girls in Tech Jobs Board is a great place to start when looking for work at top companies in tech.
Upskill and continue learning
As with any industry, the key to career success is to stay passionate and continue learning. You can do this through independent research, or by seeking out more formal learning.
A great place to consider starting is the free AWS Machine Learning Foundations Course, offered through online learning platform Udacity. This is a great course to get you started in Machine Learning, whatever skill level you’re at, that teaches the fundamentals of machine learning, steps involved in machine learning process, and real world examples of problems solved by machine learning.
As an AWS collaborator, Girl in Tech is so excited to see AWS award 425 top students with free scholarships for the entire AWS Machine Learning Engineer Nanodegree program, with enrollment open through June 23, 2021!
This initiative will help remove barriers to skills training in Machine Learning, and cultivate the next generation of Machine Learning leaders, including those from underrepresented backgrounds – including women, Black, Latinx, Indigenous, and People of Color.
Register for the AWS Machine Learning Foundations Course today.