From Fiction to Fiduciary: Reframing AI Trust in Mental Healthcare
Research
GW TAI supports and coordinates the university’s leading research on trustworthy AI in systems and for society. We focus on impact-driven, interdisciplinary research that brings a systems perspective to understanding and building trust in AI within its broader social context. Our work connects systems design decisions to strategies for the governance of AI systems and the data on which they are trained and deployed. GW TAI research spans highly technical applications as well as legal and policy frameworks, applying these core themes across diverse domains including healthcare, transportation, energy, and education. Explore a snapshot of ongoing GW TAI projects below.
How can AI accelerate discovery? Extend access to care in underserved areas?
How will increased autonomy change labor (e.g. robotaxis)? Ensure safety?
How will increased energy demands vs efficiency affect sustainability goals?
How do we ensure that AI-enabled systems continue to behave as intended once deployed in contested environments?
SENECA: Small-Sample Discrete Entropy Estimation via Self-Consistent Missing Mass
Professor H. Howie Huang
The epistemologies of trust: conflicting worldviews in the "Trustworthy AI" discourse
Professor David A. Broniatowski, Professor Alexa Alice Joubin, Professor Susan Ariel Aaronson, Professor Y. Tony Yang, Professor Neal Sikka, Professor Lorien C. Abroms, Professor Aya Zirikly, Professor Zoe Szajnfarber