Kellie Owens

Bio:
Dr. Kellie Owens’s research focuses on the people and processes that make AI work responsibly, safely, and accurately. Dr. Owens leads the development of a "Responsible AI" checklist and governance system at NYU Langone.

Abstract:
Background: Despite the increasing implementation of artificial intelligence (AI) and machine learning (ML) technologies in healthcare, their long-term safety, effectiveness, and fairness remain compromised by a lack of sustained oversight. This study explores the phenomenon of a “responsibility vacuum” in AI governance, wherein maintenance and monitoring tasks are poorly defined, inconsistently performed, and undervalued across healthcare systems.
Methods: We conducted and analyzed semi-structured interviews with 21 experts involved in AI implementation in healthcare, including clinicians, clinical informaticists, computer scientists, and legal/policy professionals. 
Results: Participants widely recognized that AI models degrade over time due to factors such as data drift, changes in clinical practice, and poor generalizability. However, monitoring practices remain ad hoc and fragmented, with few institutions investing in structured oversight infrastructure. This “responsibility vacuum” is perpetuated by institutional incentives favoring rapid innovation and strategic ignorance of AI failures. Despite these challenges, some participants described grassroots efforts to monitor and maintain AI systems, drawing inspiration from fields such as radiology, laboratory medicine, and transportation safety.
Conclusions: Our findings suggest that healthcare systems deprioritize the maintenance of AI tools, creating a governance gap that may lead to patient harm and inequitable outcomes. Addressing this responsibility vacuum will require formalized accountability structures, interdisciplinary collaboration, and policy reforms that center long-term safety and equity.