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Atom | Using Machine Learning in Safety-Critical Applications: Setting a Research Agenda
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Using Machine Learning in Safety-Critical Applications: Setting a Research Agenda
Rolling Submissions
NASEM
Description
A National Academies of Sciences, Engineering, and Medicine study will explore the trustworthiness of machine learning (ML), especially very large or complex models, in safety-critical applications. The study will consider such questions as:
What are core principles of trustworthiness in safety-critical systems? What adaptations are needed to accommodate ML models?
What metrics of trustwort...
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Eligible Applicants
- Unrestricted
Additional Eligibility Information
No specific eligibility criteria are mentioned for applying. It appears to be an exploratory study in progress.
Funding Type
other
Categories of Funding Activity
- Computers and Information Technology
- Engineering and Technology
Grantor Contact Information
Tho Nguyen; Telephone: (202) 334-2293
Grantor Email
thonguyen@nas.edu
Other Grantor Information
The National Academies of Sciences, Engineering, and Medicine are private, nonprofit institutions that provide independent, objective advice to inform policy with evidence, spark progress and innovation, and confront challenging issues for the benefit of society. They are comprised of three academies: the National Academy of Sciences, the National Academy of Engineering, and the National Academy of Medicine. The organization convenes hundreds of conferences, workshops, and symposia annually, bringing together leading experts from academia, public, and private sectors. Their work focuses on providing high-quality, evidence-based consensus on complex scientific, engineering, and health-related challenges. They produce peer-reviewed reports, publish scientific journals, and serve as a trusted source of expert advice for policymaking. The National Academies mobilize approximately 6,000 top experts each year who volunteer their time and knowledge to conduct policy studies, workshops, and other activities aimed at addressing critical issues facing the nation and world.
Additional Information
The committee's report will describe the present state-of-the-art in approaches to engineering safety-critical systems and identify research to improve the safety of systems relying on machine learning. It will provide findings and conclusions but not recommendations. Additional URLs:
Supporting Link
https://www.nationalacademies.org/our-work/using-machine-learning-in-safety-critical-applications-setting-a-research-agenda
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