Optimizing the Power of AI for Gender Equity on the hello, Human Podcast

Our podcast, hello, Human, offers an open forum to discuss the latest topics in artificial intelligence (AI) and how it’s being applied in the real world. We talk with not only the pioneers of AI, but also those who are putting AI to work transforming businesses, finding novel solutions to age-old problems, and advancing what humans can accomplish.

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Episode 11 - Optimizing the Power of AI for Gender Equity

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In episode #11, we talked with Katica Roy of Pipeline Equity, an intelligence-based platform that connects improved gender equity to improved financial outcomes. Pipeline’s solution uses AI to encourage action against the gender biases costing $2 trillion each year in the U.S. alone. A former programmer, user interface/user experience designer, and data analytics expert, Katica turned a professional experience with gender bias into a tool that gives businesses the data to eliminate bias from the workplace. She’s also an entrepreneur, thought-leader, and frequent editorial contributor and speaker. 

Katica was interviewed by our VP, Marketing, Christelle Flahaux, and myself, Elizabeth Mitelman, as part of our Women in AI series. To start, Katica was asked how she got interested in these efforts against gender bias in the workplace. She shared her story of when a male colleague was assigned additional responsibility and received a pay raise, while she was assigned even more responsibility in parallel, but with no pay increase. 

“(I) called HR, my new boss, and said, hey, this is great, very excited about it, but how do you want to make me whole in my compensation?,” Katica recounted. “Nothing was really done, so it was a couple of months back and forth. I thought, there’s got to be something that makes this illegal.”

Katica found the Lilly Ledbetter Fair Pay Act, but still had to fight to eventually receive an equitable raise and back pay. 

“Certainly, it was a story of success,” said Katica. “But the question that it led me to was why did I have to spend my time researching my rights in order to be treated fairly? It was really in that moment that the journey to ultimately founding Pipeline began.”

Finding a Data-Driven Solution to Bias

During her research, she discovered that breadwinner mothers have the largest gender pay gap of any cohort in the U.S., making just 66 cents for every dollar earned by a man in an equivalent role. She started Pipeline to use that kind of data to influence the people decisions made by organizations. What makes it so compelling is that it takes away the risk women frequently face when speaking up, a reality women know well. 

“Women inherently understand the risk of speaking up,” Katica explained. “We have experienced this long before we ever got to the workplace. We experienced it in childhood, in school. It’s not necessarily that women don’t want to be uncomfortable. It’s that they understand the downside of speaking up.”

Katica knew that, to move an issue within an organization, it had to be tied to financial success. So, she used her background in data science and programming to create a solution. In studying data from 4,000 companies, Katica found that a 10% increase in equity across gender plus race, ethnicity, and age created as much as a 2% increase in revenue. But, she also knew huge global enterprises needed technology to scale gender and diversity initiatives. Connecting the three—financial performance, data, and technology—formed the basis of Pipeline.

“CEOs who are not focusing on actively increasing equity are actually constricting their economic footprint,” said Katica. “You either have the choice to move toward equity or not. What we ensure is that each of those decisions is actually equitable for companies and also tied to greater financial performance.”

Using Data for Global Equity

Part of Katica’s data-driven approach is her 2021 Retrospective on gender equity. It puts a global spin on equity and looks at the last 10 years of progress, or lack thereof, in related topics, which impact everyone. For example, military and intelligence organizations see low education rates for young girls as a sign of pending instability and conflict, according to Katica. That obviously relates to overall economic progress and growth, too. Add to that the massive reduction of women in the labor force during the pandemic and the increasing gender pay gap, coupled with gender bias, transcend fairness. 

“We’ve got a widening gap at a time when women and their families can least afford it,” Katica said. “Women are the breadwinners in 40% of U.S. households. There are 16 million breadwinner moms, 28 million kids. The gender pay gap for breadwinner moms is the largest gender pay gap of any cohort of women. That is an issue for millions of families.”

Katica is using these types of data points to get more organizations talking about, and fixing, inherent issues with gender bias. Even more, it’s helping her give back to the global community by shining a light on the economic impact of gender equity. She sees the technology behind Pipeline eventually being used to ensure equity for everyone and in every decision. 

“If we make more money, it means that tens of thousands, hundreds of thousands, millions of people have actually also made more money because of our platform,” said Katica. “They have experienced a more equitable world. That then ties to growing the economy, which is good for everyone. For us, the money piece is not an end. It is a means to a much bigger end.”

And, in the end, it’s the data that does the talking to help prove the value of diversity, fairness, and inclusion. Well, the data and some magical algorithms. 

“It’s not magic, it’s math,” said Katica. “It’s just math.”

Hear the Entire hello, Human Episode

Hear our discussion with Katica in its entirety, including how she influences presidents, works to combat the motherhood penalty, and does it all by grounding herself in the data, on the hello, Human podcast. And, subscribe to hello, Human on your podcast app of choice, or read this episode’s full transcript.