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Sunday, April 11, 2021 | 12:55 am
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Women in Tech: How To Embrace Diversity and Equity

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Women in Tech: How To Embrace Diversity and Equity

Life is not linear. That was an early life lesson from my college days when my presumed law school track was interrupted by some serious rethinking.  Like so many others, school exposed me to different ideas and ways of looking at the world. A class on military dictatorships and how foreign intervention impacted violence and development in those countries really sparked my desire to understand not only why the world is the way it is, but how we can stop perpetuating systems of inequity.

My first job at a Fortune 500 strategy and technology consulting firm was the ideal place to land. It was there that I became keenly aware that the world’s most serious problems deserve reflection, attention, and action by many kinds of people.

This reinforced my own personal ideas about how we as humans should be supporting and solving problems for each other. Now as a co-founder of Modzy, a ModelOps platform to run, use and manage artificial intelligence, it’s become a driving force of my work and my life.

These challenges are pervasive in building technology, specifically AI, today. Even though AI has already infiltrated nearly every aspect of our daily lives and the business front, it’s just getting started. A recent McKinsey study projected US$13tn of global economic growth from AI by 2030. Most of that growth will be in first-world countries which will only widen the disparity gaps we’re already seeing today.

At Modzy, we are acutely aware of these realities, and the pursuit of ethical AI is one of the reasons our company came into being. All AI is built on data, and data is inherently biased. You can’t remove the bias from the data, nor can you expect that technology is necessarily going to change that.

Humans need to be in the equation, putting mechanisms in place to address bias, equity, and fairness. Without that, we’re going to keep amplifying biases in data and impact marginalized populations.

When you think about managing people in the realm of AI and machine learning, you can’t have all the stakeholders in their own boxes – data scientists building the models, leadership looking at productivity and profitability. There needs to be communication across teams to have difficult conversations about what we will build and what we won’t build.

We must continue to advance and evolve ahead of the machines and the software to ensure the right technology is developed for the right reasons. And that means acknowledging the reality we’re in to make the future better for all of us.

Let’s face it. The lack of diversity among technology developers is part of the problem. This reality was driven home for me when, pre-pandemic, I was speaking at tech conferences. At each one, I’d look out at a sea of people, predominately white, male people, and wonder, what’s wrong with this picture? Fortunately, the tide is shifting slowly but surely. We need more women, people of color, and people from different walks of life to build technology for the future.

As noted, life hasn’t proceeded on the trajectory I once imagined. Yet here I am, working in one of the most exciting, revolutionary fields of our time. Trust me, I often pinch myself, and sometimes I struggle with the weight of what we’re trying to accomplish and how to best use my voice.

Our ability to speak out is so important. We’re at this precipice with AI and we don’t take that responsibility lightly. Often being the youngest and one of the few women in the room, I feel it’s sometimes hard for me to speak up. So, I think often of a sage piece of advice from a colleague and mentor who had observed my reticence. If not you, then who, he asked me.

It was so meaningful to me to be recognized and empowered to speak my mind. I know it’s not the norm, but I sure wish it was. We need more inclusive people and places where we’re propping others up to participate and contribute.

As leaders, we must champion ethics principles and ensure everyone is on the same page. We must have hard discussions about what applications of technology meet our principles, and what violates them. We must create channels for recourse when mishaps do happen and incentivize all stakeholders to help ensure systems are accountable and reliable. It’s time to move beyond ideas to action.

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