Safe Learning-Enabled Systems

The summary for the Safe Learning-Enabled Systems grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Science Foundation, which is the U.S. government agency offering this grant.
Safe Learning-Enabled Systems: As artificial intelligence (AI) systems rapidly increase in size, acquire new capabilities, and are deployed in high-stakes settings, their safety becomes extremely important. Ensuring system safety requires more than improving accuracy, efficiency, and scalability: it requires ensuring that systems are robust to extreme events, and monitoring them for anomalous and unsafe behavior. The objective of the Safe Learning-Enabled Systems program, which is a partnership between the National Science Foundation, Open Philanthropy and Good Ventures, is to foster foundational research that leads to the design and implementation of learning-enabled systems in which safety is ensured with high levels of confidence. While traditional machine learning systems are evaluated pointwise with respect to a fixed test set, such static coverage provides only limited assurance when exposed to unprecedented conditions in high-stakes operating environments. Verifying that learning components of such systems achieve safety guarantees for all possible inputs may be difficult, if not impossible. Instead, a system's safety guarantees will often need to be established with respect to systematically generated data from realistic (yet appropriately pessimistic) operating environments. Safety also requires resilience to “unknown unknowns”, which necessitates improved methods for monitoring for unexpected environmental hazards or anomalous system behaviors, including during deployment. In some instances, safety may further require new methods for reverse-engineering, inspecting, and interpreting the internal logic of learned models to identify unexpected behavior that could not be found by black-box testing alone, and methods for improving the performance by directly adapting the systems' internal logic. Whatever the setting, any learning-enabled system's end-to-end safety guarantees must be specified clearly and precisely. Any system claiming to satisfy a safety specification must provide rigorous evidence, through analysis corroborated empirically and/or with mathematical proof.
Federal Grant Title: Safe Learning-Enabled Systems
Federal Agency Name: National Science Foundation (NSF)
Grant Categories: Science and Technology
Type of Opportunity: Discretionary
Funding Opportunity Number: 23-562
Type of Funding: Grant
CFDA Numbers: 47.070
CFDA Descriptions: Information not provided
Current Application Deadline: May 26th, 2023
Original Application Deadline: May 26th, 2023
Posted Date: March 21st, 2023
Creation Date: March 21st, 2023
Archive Date: February 15th, 2024
Total Program Funding: $20,000,000
Maximum Federal Grant Award: $9
Minimum Federal Grant Award:
Expected Number of Awards:
Cost Sharing or Matching: No
Last Updated: March 21st, 2023
Applicants Eligible for this Grant
Others (see text field entitled "Additional Information on Eligibility" for clarification.)
Additional Information on Eligibility
*Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. -Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus. *Who May Serve as PI: Employees of Open Philanthropy and Good Ventures may not participate in proposals submitted to this initiative, including as unfunded collaborators, via letters of collaboration or support, or via any other means.
Link to Full Grant Announcement
NSF Publication 23-562
Grant Announcement Contact
NSF support
[email protected]
If you have any problems linking to this funding announcement, please contact the email address above.
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