Visible to the public 2022 NSF SafeTAI WorkshopConflict Detection Enabled

Visible to the public 

Welcome to the 2022 NSF SafeTAI Workshop Homepage!

The aim of the 2022 NSF Workshop on Safety and Trust in Artificial Intelligence (AI) Enabled Systems is to identify emerging issues, challenges, basic research questions, and potential approaches to study and address safety and trust in AI-enabled systems across application domains. The workshop will bring together researchers from academia, industry, and government research that are working in areas such as AI, machine learning (ML), formal methods, programming languages, safety, and beyond, as well as application domains such as autonomous systems, business, and beyond. The main deliverable of the workshop is a report summarizing the discussions and findings of the workshop, which will be made publicly available following the event.

The workshop will be a two-day event Thursday, September 22nd - Friday, 23rd 2022 held virtually
over zoom with a keynote speaker each day and parallel breakout sessions.

Fostering the development of collaborations and the exchange of ideas between the formal methods, AI/ML, and broader research communities is critical and may lead to advances in methods that improve both AI/ML and formal methods, as well as the broader concerns about safety and trust of AI-enabled systems across science, engineering, and society. The workshop encourages participation and perspectives from persons coming from underrepresented groups.

K E Y N O T E    S P E A K E R S

DR. KATHLEEN FISHER assumed the role of Office Director for DARPA’s Information Innovation Office (I2O) in May 2022. In this position, she leads program managers in the development of programs, technologies, and capabilities to ensure information advantage for the United States and its allies, and coordinates this work across the Department of Defense and U.S. government.


DEBORAH RAJI is a Mozilla fellow and CS PhD student at University of California, Berkeley, who is interested in questions on algorithmic auditing and evaluation. In the past, she worked closely with the Algorithmic Justice League initiative to highlight bias in deployed AI products. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on various projects to operationalize ethical considerations in ML engineering practice. Recently, she was named to Forbes 30 Under 30 and MIT Tech Review 35 Under 35 Innovators.


Sponsored by National Science Foundation Award 2231543