NIST leadership commented on the need for a tailored approach to artificial intelligence risk management, and how understanding an industry is key to anticipating AI system risks.
On the heels of the launch of its debut Artificial Intelligence Risk Management Framework, the National Institute of Standards and Technology continues to posture its standards-setting approach to prioritize human engagement with emerging technologies.
One detail that NIST researchers may target in forthcoming editions of their AI RMF guidance is a set of more tailored recommendations on AI and machine learning algorithms for different industries, according to an agency official.
Elham Tabassi, the chief of staff in the Information Technology Laboratory at NIST, spoke in a Friday discussion hosted by the George Washington University and expanded upon the new AI RMF’s purpose and applications.
While the RMF was designed to be broadly applicable to a variety of use cases as a general, high-level framework, Tabassi said she ultimately thinks that AI risk management techniques should vary between different sectors and implementation is best conducted with the help of groups with domain expertise.
“AI is all about context and use case, and how either of these trustworthy characteristics…will manifest themselves in different use cases in [the] financial sector, versus hiring, versus face recognition…and they may have different priorities,” she said. “There is need for a tailored guidance to that specific technology or use case.”
She added that conversations have already begun on developing a tailored AI RMF for specific applications. An updated version of the accompanying AI playbook authored by NIST is slated to be released in Spring 2023.
“What we were trying to do is giving some general, technology-agnostic, sector-agnostic framework, but strong enough foundation that all these verticals could be built up,” she said.
Tabassi also noted that, in the open-comment period to collect feedback and input on the current AI RMF, NIST received over 400 sets of comments from people in government, academia and the private sector.