Kiana Jafari
Postdoctoral Researcher
I'm a Postdoctoral Researcher at Stanford University, working at the Stanford Intelligent Systems Laboratory (SISL) with Professor Mykel Kochenderfer. I also serve as Executive Director of the Stanford Center for AI Safety, lead Human Factors Research and Lab Operations at the Stanford Flight Simulator Laboratory, and am Assistant to the Editors-in-Chief at JAIR. Before joining SISL, I completed my Ph.D. at the University of Virginia in Systems and Information Engineering, co-advised by Professors Peter Beling and Matthew Bolton.
My research centers on Human-Agent Teaming and Human-Centered AI, with a focus on designing intelligent systems that enhance collaboration and empower human decision-making. I am particularly interested in how AI can be developed to complement human strengths, preserve autonomy, and align with human values across diverse domains.

Research
Human-Agent Teaming
Designing intelligent systems that enhance collaboration between humans and AI agents, preserving autonomy and complementing human strengths.
2 projectsAI Evaluation & Adversarial Testing
Developing rigorous methodologies to assess AI system capabilities, limitations, and vulnerabilities through systematic testing.
4 projectsResponsible AI
Advancing governance frameworks and organizational practices to ensure AI development aligns with human welfare and societal benefits.
2 projectsAI Safety
Researching methods to ensure AI systems operate reliably, safely, and in alignment with human values across critical domains.
2 projectsRecent Projects
The Doctor Will (Still) See You Now: On the Structural Limits of Agentic AI in Healthcare
A qualitative study based on interviews with 20 stakeholders examining how agentic AI is defined, evaluated, and constrained in healthcare, identifying three mutually reinforcing tensions: conceptual fragmentation, an autonomy contradiction, and an evaluation blind spot.
Expert Evaluation and the Limits of Human Feedback in Mental Health AI Safety Testing
A mixed-methods study examining inter-rater reliability among three psychiatrists evaluating 360 LLM-generated mental health responses, revealing systematic expert disagreement driven by incompatible clinical frameworks rather than measurement error.
Responsible AI in the Global Context
A global survey-based study exploring responsible AI practices across 1000 organizations in 20 industries and 19 regions, defining a conceptual RAI maturity model.
Latest News
Our Paper "The Measurement Imbalance in Agentic AI Evaluation Undermines Industry Productivity Claims" got accepted to NeurIPS 2025 by our reviewers and the area chair but got REJECTED by NeurIPS.
Had the wonderful opportunity to moderate a panel bringing together leaders from Google, EveryoneAI, and IST to discuss global AI safety and policy.
Our Paper "An Adaptive Responsible AI Governance Framework for Decentralized Organizations" is accepted to AIES 2025.