Myth #1: Responsible Artificial Intelligence Isn't for Me
Updated: Mar 5
How I came to understand that Responsible AI is both achievable and necessary
Let me tell you a short story of my eye-opening to Responsible AI. It is almost as simple asking myself “If I am not being responsible with AI, then what does that mean I am doing?” Let’s just say, I didn’t like the answer to that question.
Some Context: Where I am Coming From
Bringing together business and sophisticated analytics
It has been (and continues to be!) quite the journey, I must say. As a brief introduction, I’ve had a significant career in the world of analytics as it relates to business. Based on many objectives of the companies I have been in, I could even say it has been a successful career.
Over my career, I’ve often looked to wear two hats. One was that of the “analytics guru”, the other of the “business strategist”. The challenge I gave (and give) to myself it is to bring together these two sides of a business.
I feel for the data scientists who must dig deep into data to make the best of what information they have – often in timeframes shorter than ideal.
I also have empathy for the business strategist who is under pressure to “deliver results now”.
Finally, I am a human who believes in trying to continuously help the world become a better place and providing people opportunities. I’ve studied history and know this is not easy, but I like challenges!
The Fundamentals of Analytics (and AI)
What hasn’t Changed
While admittedly, in the big picture, I am still late to the game, the analytics world I lived in at the beginning of my career was extremely simple as compared to today. It always amazes me, though, that regardless of the terminology of the time (advanced analytics, data science, machine learning, artificial intelligence), the fundamentals I learned and follow still hold.
Even if what I learned, back in the day, as a “variable” is now called a “feature” and more powerful analytic techniques such as Neural Nets, XGBoost and Random Forests, are now realistic, it often still comes down to good data (garbage in = garbage out) and robust assessment of model performance related to business objectives.
The Real Evolution in Data and Analytics
The exponential impact and potential for reinforcing bias of AI
What is different, though, is the exponential explosion of the impact of analytics – and our needed awareness and responsibility in responsibly employing it. It’s hard to keep up with all the figures and pace, but many sources quote statistics such that “90%+” of data in all of history is created within the last 2 (or maybe less) years…. And we tend to use all this data - often automated into just about every business decision that is made.
So as I thought about all this data and all these decisions that are being made – offering products to different demographics, deciding who is approved for credit, determining where to locate medical services, choosing who to bring in to interview for jobs – two key things hit me like a strike of lightning.
If I am not responsibly looking for unintended outcomes from AI, what is really happening?
If there are unintended outcomes, what systemic issues does it potentially create that reinforces the potential of future similar unintended outcomes?
Why Responsible AI is for Me (and I hope you)
It's time for inclusiveness in AI
It was that second question that really stuck with me and led me to creating Eagna and an inclusive system for designing Responsible AI. Wearing my data scientist hat, I understood that the immense data we are creating continually becomes more and more biased. With my business strategist hat, I understood historical lack of awareness and prioritization of societal impacts in business goals.
With these two hats on, the question of “How do we solve this?” came to mind. As fortune would have it, there is a way. There is an immense amount of brilliant thought across the business and academic worlds that gives us guidance. While, as expected, it is not necessarily easy and there aren’t perfect answers, it is definitely doable. That’s why Responsible AI is for me… I believe we can best remove bias and mitigate unintended outcomes by bringing committed teams together. I love the opportunity to help make that happen.
How Responsible AI is for all of us
An organizational approach to key business function
One of my favorite things in the world is all the various skills and perspectives we bring. While AI is generally more associated with more technical, analytical types of people, AI is a business function. As with any business function, we need to ensure various perspectives are solicited and involved.
We all play a significant role in Responsible AI – from product designers that understand product functionality, to marketers who understand consumer trends, to policy leads who ensure regulatory compliance, to, of course, data scientists who build algorithms informed with business strategy. Moreover, in a trend I am loving, our Diversity, Equity, and Inclusion (DE&I) organizations are being provided more influence in organizations and more than deserve a powerful voice in our Responsible AI efforts.
So beyond knowing that Responsible AI is for us, we also can now know our role in it. At Eagna, we look to take a lead in raising awareness and employing best practices of Responsible AI, but each of us can have a role in our own journey. We don't all need to be data experts to participate in AI, in fact, the perspectives on those that aren't these experts is critical because there needs to be deep understanding of the business alignment and impacts of AI. The best-in-class AI approaches and methodologies are designed to welcome in and engage diverse parties in AI development and management.
Join us on our Journey
While we are early in our journey at Eagna, we bring lots of experience and are excited about where we can all go together. The Responsible AI blog is also just beginning. Our goal is to demystify Responsible AI and make it feel inclusive and accessible to all. As we evolve, we look to cover overall trends, industries, and bring other leader voices to you. If you have an idea you’d like to collaborate on we’d love to hear from you.
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