Tell us a little about your career journey: How did you come to work in AI policy?

My career in AI policy was quite the accident. I graduated with my masters in security studies in 2019 with an offer from the intelligence community to work in counterterrorism. At the time, this was my dream job and everything I’d worked for. I was also aware that you can’t rely on the US government to conduct a swift security clearance and knew I needed to find a job that I felt equally passionate about. Luckily, an algorithm worked in my favor my last semester in grad school and I was placed in the new and highly-coveted course, AI and National Security. I was skeptical at first, but after learning about all the ways that AI could go wrong, intentional or not, I felt the same pull to protect innocent people from harm, but this time from technology, not terrorists. I landed at the Center for Security and Emerging Technology as a research analyst upon graduation, which really jump-started my career in the AI policy space.

What are some of the current AI policy challenges you’re working on?

I’m going to interpret policy from two angles, federal regulation and internal policies at companies. At EqualAI, we work with policymakers and industry leaders on how to best design and implement responsible AI governance frameworks to mitigate bias and other harms stemming from AI. We offer a few flagship programs to help accomplish this. The first is our Badge Program for senior level executives at companies that may not necessarily be deemed as “AI” companies like OpenAI or Anthropic, but are actively developing, acquiring, or deploying AI in pivotal ways and are seeking guidance on how to do so responsibly. In a landscape of uncertain federal regulations on AI, the Badge Program equips industry leaders with the knowledge on how to proactively leverage AI for  its benefits while mitigating its risks. I recently co-authored a white paper with our Badge participants from Salesforce, AWS, Google DeepMind, Verizon, Pepsi, Northrop Grumman, Microsoft, and SAS Institute that presents a responsible AI governance framework we all aligned on, and can be adopted by an organization of any size, industry, or maturity level. 

Another policy challenge I’ve been working on is educating Capitol Hill staffers on AI issues that go beyond the typical “AI 101” programming. This fall we launched our first pilot program where we brought experts to the Hill to do a deep dive on a particular issue. Our most recent session covered industry perspectives. We brought in those who are actively working on implementing responsible AI best practices in different companies so staffers could better understand the opportunities and challenges these leaders face. 

What advice do you have for those interested in a similar career path?

Try not to plan too far ahead. Every single job I’ve held in this space was the first time that job came into existence. The AI policy space is constantly evolving and expanding, so I’ve come to accept that the jobs I may have in the future don’t exist in the present day. Just do what’s interesting and engaging to you at the moment and “trust the process”.

What skills do you think are important for success in AI policy, and how could readers acquire them?

Strong oral and written communication skills are key. The ability to communicate effectively across a variety of audiences (technical and non-technical) will set you apart from the rest. I’d recommend getting comfortable and familiar with the core concepts of AI/ML as well as a basic understanding of how policy works in Congress. You don’t need to know every little detail or get into the weeds (in fact, sometimes I think people become less effective communicators when they are in too deep), but you should know common concepts, opportunities, and challenges in both worlds to navigate these conversations with credibility. 

Are there any programs, resources, or books you’d especially recommend for those interested in AI policy?

My recommendation doesn’t really fall into any of these categories, but rather, meet as many people working in this space across different orgs/sectors as possible. AI policy is not linear, there are so many players across the private and public sector. Try to truly understand each person’s perspectives, what drives or deters their work, and how these various vantage points end up intersecting or colliding.

This is part of a series of career profiles, aiming to make career stories and resources more accessible to people without easy access to mentorship and advice. If you have suggestions for what questions you’d like to see answered in these profiles, please fill out our feedback form.