Behavioral Health Clinical Review •
AI Safety • Clinical Education
I bring more than 15 years of clinical social work experience to the evaluation of AI-generated behavioral-health content, with a focus on clinical reasoning, safety, context, bias, trauma-informed practice, and meaningful human oversight.

Diagnostic restraint,
calibrated confidence,
and sound interpretation
Recognition, escalation,
proportionality, and foreseeable harm
Whether real-world barriers
actually change the reasoning
Consent, autonomy, relational impact, and non-coercive care
Cultural humility, stigma, assumptions, and structural context
Privacy, role clarity, authority, and meaningful human review
Here is a short educational presentation exploring how generative AI can support accessibility while preserving agency, privacy, clinical judgment, trauma-informed practice, and meaningful human oversight. Developed as part of my ongoing work translating emerging AI concepts into practical, clinically grounded education for behavioral-health professionals.

I bring clinical social work judgment, behavioral-health experience, training, and emerging AI evaluation work together to review, test, and improve mental-health AI content. Available for clinical AI evaluation, behavioral-health content review, rubric development, benchmark cases, and clinical education.