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Executive Interview: Beena Ammanath Boosts Women and Diversity in Tech as AI Expands




Humans for AI was founded by Beena Ammanath to increase opportunities for women and to diversify the tech workforce as AI continues its march through business. (HUMANS FOR AI)

Putting Guardrails in Place for Women and Diversity in Tech as AI Is Infused Into Everything in the Organization

Beena Ammanath is the Founder and CEO of Humans For AI, a nonprofit organization focused on increasing diversity in tech leveraging AI. She is a recognized lead and industry expert who has driven pioneering technology changes in the use of AI, Data and Analytics for several market-leading companies. She has worked as a mentor to help women and minorities enter the new economy. She started Humans for AI in 2017 to help make AI understandable to the non-tech community.

Beena is also an Industrial Board Member of Cal Poly University, where she brings the industry perspective to influence curriculum engineers. She previously worked as CTO-AI at Hewlett-Packard Enterprise. She recently spent a few minutes talking with AI Trends Editor John P. Desmond.

Beena Ammanath, Founder and CEO of Humans For AI

AI Trends: Thank you, Beena, for being here with us today. I know you have been involved with data science and AI for many years in executive management positions for several major companies. Given your range of experience, what are the top trends around AI in business today that concern you or that you see having the most impact?

Beena Ammanath: Before it was called AI, there were statisticians and people who worked on predicting trends and looking at data, looking at managing big data and really driving insights. The term AI itself has existed since 1956. What we are  seeing now is its broad application and influence in organizations across the world.

Today AI is pretty much in every industry and every sector. Everywhere you look there are AI use cases. Some industries are more mature, more ahead with their AI use cases than the others. They tend to be the ones where there is more data easily available. In my career, I have worked in different industries, different domains, whether it’s financial trading, IOT, manufacturing, field services and telecom. It’s given me a broad perspective into how the data analytics and AI space has evolved.

We see many AI use cases in the financial sector and in the big tech social media sector. AI is not as prevalent yet in legacy  sectors like industrial manufacturing, Having to look at historical data becomes a challenge when you haven’t embedded the sensors to start with.

I have also seen a shift in the focus of AI. The focus up to two or three years ago was very much around value creation, doing POCs [proof of concept projects] and scaling up those POCs—“How do you actually drive value from the data?”—very focused around value and insights.

Now I see more and more urgency happening about the ethics part of AI, about what could potentially go wrong, and how do you put the guardrails in place? I have spoken about ethics for a few years now. It’s something that I worry about; I passionately care about making sure that what we build is the best thing for humankind. And I am so heartened to see more and more companies actually bringing up ethics earlier on in the conversation compared to say, two or three years ago.

You were the founder of Humans for AI in 2017. Can you say why you founded the group and how’s the work going there?

I have built a few data and AI teams and it tends to be very monochromatic. We all know there is a problem about lack of women in tech and diversity in tech. And I see it even more starkly reflected in the data and AI teams.

I worry about it, because we all have heard about how AI can be biased based on who builds it. Our biases get embedded into the AI system that we build. And the only solution to that is to bring more diversity to the table. And when I say diversity, I don’t mean just the gender part of it, but bringing in people from different backgrounds. Whether it’s a different economic background, professional background, educational background, people who think differently than you. That’s very important.

So diversity from every aspect is very important for us to build robust AI products and solutions. And having built these teams, I’ve noticed it’s really hard to find women or people of color or people from different education backgrounds to be part of the AI teams. And I see an opportunity for us to actually fix the problem. I also think that if we don’t fix the problem, then AI just won’t reach its full potential.

Humans for AI was founded with a single mission of increasing diversity in AI. Women tend to be the largest minority group, but it’s also about bringing in more people of color, people with different perspectives to the AI table—make all humans part of the AI narrative. So the way we are approaching it is by providing very focused AI literacy programs to diverse and minority groups.

We know that an AI team is not just the folks who have the PhDs in machine learning. Even though that’s what is communicated, I know they need to be surrounded by very good software engineers, very good designers and testers and project managers. So the way Humans for AI is approaching it, is by bringing in diversity in all the other roles that surround the data scientists.

We have a few programs as part of this initiative. (1) We have set up a foundation along with UC Berkeley called the Alliance for Inclusive AI; the foundation will be giving scholarships to women and minorities who want to study AI at UC Berkeley.


(2) We are also launching a virtual conference in May, which is going to be about teaching AI by profession. We will have a series around AI for specific professions: AI for nurses, AI for physical therapists, AI for elementary school teachers, for example. (3) We have Humans for AI ambassadors all over the world, who organize local meetups to help drive that mission. We are also partnering with other nonprofits who are more focused around teaching AI and coding to women and girls and minorities. So if anybody wants to study coding, we send them to our partner organization. All of this is anchored on a virtual community bring together the AI experts and novices on one virtual platform to interact and grow AI together.

It’s quite exciting. We are a bit behind the curve on getting more diversity into AI, but  this is my attempt to move the needle on it.

Very good. On the topic of how far along we are in the practice of AI, how mature is the industry today?

I have been on the board of several startups and I get invited to speak at board meetings as an expert on AI. I have seen a lot of interest. The trend has changed from what we can do with AI, to what we should be thinking about holistically to scale AI, to make sure that we are reaching the full potential with AI in our organization.

That includes the ethics piece as I mentioned earlier, but also, how you infuse AI to drive more value within the organization. It’s no longer just about being able to build better products, but how we can  use AI within every  functions in an organization, like finance or HR or legal. So being able to infuse AI across the organization, is a much more discussed topic today, compared to a few years ago. I also see many companies setting up separate innovation groups to look at not only just AI, but at other technologies like AR, VR and blockchain. AI is certainly a big part of the whole digital landscape, but looking at how the whole evolution impacts business is a much more talked about topic today.

I see you are an advisor to the California Polytechnic State University, College of Engineering. How’s that going?

I have been on the Cal-Poly industrial advisory board for quite a few years now. What we teach the next generation of engineers has to be very much relevant to what we are seeing in the industry.

The best way to shape the thinking of future generations, is by getting more actively involved in how the curriculum is shaped, how it is delivered, and bringing in industry engagement. I have definitely learned a lot by engaging with Cal-Poly, and I hope my input is helping to shape the future of tech curriculum.

Sounds good. How do you believe the workforce is responding to the challenge of learning about AI, while not getting overrun by automation?

We definitely see a lot of hype around AI. We are used to seeing headline articles about AI taking away everybody’s job and jobs being eliminated completely. The reality is, that is hype. There is a grain of truth, in that certain jobs will get eliminated, but AI is also creating a ton of new jobs. And you don’t hear so much about that. You hear a lot about jobs getting eliminated.

I’m a history buff. When I look back, the jobs that existed 100 years ago look completely different from the jobs that exist today. Not only from a title perspective or the role perspective, but even how the job was done was very different 100 years ago compared to today. Or even 50 years ago compared to today.

So there will be job elimination, and there will be job creation as well. I have definitely seen a bigger need for more people with technical chops. Think about all the sensors getting embedded into all the physical devices we have. Not only our phones, but our cars, our microwaves and our refrigerators – more and more compared to even a few years ago.

We need more hardware engineers. If you search, you will find job descriptions for sensor cleaners for the automotive industry. That’s a new job. If you think about the traditional AI technology teams, more people are needed for the data curation and the validation of the AI results. Those jobs didn’t exist 20 years ago. More roles will get created. As automation takes over some of the more mundane and boring tasks, we will see many new jobs getting created as part of the AI journey we are on.

Do you have any advice for young people or mid-career people interested in pursuing AI? Where should a college undergrad start? What would you recommend for an early or mid-career professional?

If you are already on a career track within technology, it is absolutely important that you know about AI and you have basic AI literacy chops. It’s like any other technology, but I do think basic AI literacy is important for any professional. Anybody who is part of the workforce today needs basic AI literacy, just because AI is so infused into everything that we do. No matter what your job is, you’re going to be using AI in some form or the other. So I think basic AI literacy is super important. Lots of curriculum is out there and organizations like Humans for AI are really focusing on making AI literacy more and more accessible.


If you are a student who wants to go down the AI career path, there is the traditional pure data scientist part of studying machine learning and AI, going very deep into that. But there’s also, as I was saying earlier, a lot of demand for people who design the AI solutions, who make them more accessible – UX designers for AI and testers in QA for AI for example. Many paths can enable you to work in AI and the roles can be different. Even as we speak, new roles and titles are getting created in the AI job market.

Learn more at Humans for AI.


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