In the artificial intelligence / machine learning (AI/ML) world, only 12% of women design the code that touch all our lives. We know that machines learn from current behaviors and the people who design the code. Since 88% of them are men, the bias is entrenched into everything that is written. #notagoodidea. The bias from this approach is eminent and is noted in many industries. The only way this can be addressed is having more women in STEM at the helm of these codes, bringing diversity of thought into everything that is designed and coded.
This reality is all too real for *Sarina, a 10-year veteran in the machine learning world. After graduating with her PhD in science, she entered the world of ML. Despite a strong STEM background in math and programming, she immediately met a wall of resistance.
As Sarina says, “After joining the AI/ML workforce, my ideas were ignored, or if a male repeated them, everybody would think they were great ideas. I was often the only female in the meetings, and I soon realized that the most promising projects or opportunities weren’t assigned to me.”
Something had to give. With the support of her partner, who is in the same field, and Gotara’s just-in-time career growth advice for women in STEM, Sarina says she received the support she needed not to give up and to continue pursuing her passion. “I want to make an impact and keep working to make this change!” she says.
“A few years ago, AI seemed like a far reach and a glimmer of something powerful that our business could use to support the customers we serve. But, instead – the future is now. It’s time to get excited and engaged with AI.” – Natasha Porter
Sarina’s situation is one that Natasha Porter, Chief Customer Officer at Benchmark Digital, understands. In a recent TARATALK conversation, she discussed various aspects of AI/ML in the ESG (Environmental Social and Governance) space and beyond with Gotara’s founder and CEO, D. Sangeeta.
In the ESG space, Benchmark Digital has been supporting customers for over 25 years via its digital platform. In addition to the customer’s data on employee concerns, safety observations, injuries, and incidents, a high volume of data is available in the public domain. Benchmark is beginning to leverage that data to provide insights to its customers. “Benchmark wants to provide these insights proactively to our clients, so they have it, just-in-time when they need it,” explains Natasha.
Data analytics is typically relied upon to provide the above service, but the insights are limited and require many human hours! Due to limited resources, the time involved, and the inability to hypothesize future state, relying solely on data analytics is limited, and AI/ML needs to be leveraged.
“This field requires you to play with the data until you get to a point where it starts to provide meaningful insights,” explains Natasha. ”You must expose the data to the models many times and iterate. And what you learn may surprise you because it’s not what your gut instincts may have told you.”
In addition, you need to constantly clean the data and re-assess modeling hypotheses as past solutions create bias into the future. So, now the question is, how do we bring the resources to the table to remove or minimize bias in the algorithms, codes, and insights? And hence, how do we get more women into AI/ML space?
The tech space, and especially AI/ML, has an image problem. Based on her experience recruiting and mentoring women, Natasha agrees. It’s seen as a nerdy field that lacks purpose and is both unwelcoming and unappealing to women. In our TARATALK, Natasha and Sangeeta discussed five ways to bring gender parity to the AI/ML field for women in STEM.
AI/ML: What‘s the purpose?
Working in this space can be ambiguous. You may not know what you‘ll discover or what insights you may want to deliver. However, there‘s always a goal that you or your organization will be trying to achieve. At Benchmark Digital, Natasha says the company‘s purpose is to “Keep People Safer” by providing recommendations when customers need it that can reduce or eliminate mistakes. Now, that is a powerful purpose. Who wouldn‘t want to keep people safe?
Once you know the why, you‘ll understand how AI/ML influences the what and how you‘ll achieve your goals. When you‘re trying to attract people to work in this field, or prospective clients, it‘s key that you share concrete examples that illustrate how AI/ML will make a purposeful difference.
AI/ML: Do you need a particular degree to get into this world?
Only a few universities award degrees in Artificial Intelligence / Machine Learning and Data Science. Today, most people have some type of science or engineering degree, and they learn AI/ML on the job. If you want to get into this field in your organization, raise your hand and volunteer for a stretch project. And, if you already have an AI/ML background, be sure to explore your opportunities because the demand for AI/ML expertise is high.
Sarina, the ML expert mentioned earlier, has been recruiting candidates for her teams for almost eight years. “Having someone with a strong background in math/science, good programming skills and who is a fast learner are the best skillsets I look for, regardless of the specific degree they got in school,“ she explains.
AI/ML: Amplify the need to attract potential candidates via social media and other channels
It starts by clearly articulating your purpose in the job post or your conversations with potential candidates. They need to understand what a cool opportunity the position is and how it’s their chance to help change the world. So, ensure when you advertise a position, or you are talking to a candidate that you start with the purpose, then explain why this can be cool and how they can change the world and ride this rocket ship of AI/ML.
Some great places to scout talent are gatherings like the Grace Hopper Conference (GHC) or professional groups like “Women in ML” or Gotara.
Be sure that your interviewing portal/platform that selects candidates isn’t based on biased algorithms. For example, if the machine relies on past data, where 88% of people in the field are men, it will train the algorithm to prioritize the candidates with a similar profile.
AI/ML: What‘s the potential payoff! ($$)
It‘s considerable! If you‘re the employer, be sure you highlight the potential economic upside. And, if you‘re the job seeker, be sure you know what you‘re worth. Communicate that these roles are a lot more lucrative than other roles. Why won’t college graduates and experienced professionals want that? Money may not be THE motivator for many, but it certainly helps make this field very attractive to candidates.
AI/ML: What‘s the best way to remove gender bias in STEM?
The answer is simple: hire more women in STEM. Diversity of thought is critical in a field that will influence so many facets of our lives, from healthcare to e-commerce to aerospace and automotive industries. We all lose if diversity – from women and other minorities – isn’t brought into the mix. It doesn’t take a complicated algorithm to tell you that the lack of diversity is not the best option or outcome for humanity.
For example, there are many paths to success, women may choose a different path to solving the same problem. This approach could be as good or better than what’s in the archival data bank. But the algorithm could reject that as not the best option.
Having more females as well as other minorities in the field will help bring new points of view to the table. This leads to the development of unique strategies for problem-solving that can help overcome some of the existing problems. For instance, in the above example, we can address bias by understanding how the datasets were weighted for the model built and how the model selection and evaluation were actually conducted. Although this might seem a very simple variation, it could have had a very big impact on the outcome.
Together, let us make this field enticing and attractive to STEM+ women and get more women on this rocket ship. Let’s change the way AI/ML is done today, removing any bias that exists at the core of AI/ML.
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