When people learn I’m both a Licensed Marriage and Family Therapist and a data scientist who builds LLM evaluation pipelines, the first question is usually some version of “How did that happen?” The honest answer: the combination wasn’t an accident. It’s the product.
The I/O Psychology Bridge
My M.S. in Industrial/Organizational Psychology sits exactly at the intersection of behavioral science and organizational analytics. I/O Psych is about understanding how people behave in systems — how incentive structures shape decisions, how cognitive biases lead to systematic errors, how group dynamics amplify or suppress signal. These are the same problems that show up in AI alignment, fairness evaluation, and responsible AI governance.
When I evaluate an LLM for hallucination, I’m applying the same epistemological rigor I use in clinical assessment — checking whether the system’s output is grounded in reality, identifying the conditions under which it fails, and designing interventions that make the failure visible before it causes harm.
Why This Matters for AI
Most data scientists who work on responsible AI come from either a pure engineering background (strong on implementation, weaker on the behavioral theory) or a policy background (strong on frameworks, weaker on production systems). Very few have clinical training — the kind where you learn to assess risk in real time, manage ambiguity in high-stakes situations, and design interventions that actually change behavior.
My clinical license means I can speak credibly to behavioral AI ethics, human-centered transparency documentation, and fairness frameworks in a way that most engineers can’t. And my 20 years at Lockheed Martin mean I can actually ship those frameworks in production environments where getting it wrong has financial and regulatory consequences.
The Roles Where This Wins
The combination creates a genuine differentiator for a specific set of roles: responsible AI governance, trust & safety, behavioral-health technology (where clinical credibility opens doors that engineering credentials alone don’t), and any AI leadership position where the human side of the equation matters as much as the technical side.
The future of AI isn’t just about making models more capable. It’s about making them trustworthy. And trustworthiness requires understanding both the system and the humans who use it.
I hold four degrees: M.A. Counseling Psychology, M.S. Industrial/Organizational Psychology, B.S. Corporate Finance, and B.S. Business Management. More on my About page.
Leave a Reply