Grants and Contributions:
Title:
An empirical framework for robust machine learning systems and its application in AI4L
Agreement Number:
1026307
Agreement Value:
$137,500.00
Agreement Date:
Feb 13, 2025 - Mar 31, 2026
Description:
Machine Learning (ML) technologies have been widely adopted in many mission-critical fields to support intelligent decision-making with superior performance. With the success of these new technologies, the application of ML introduces novel and significant threats to AI-powered systems. Policymakers around the world have made a number of ongoing efforts on regulation enactment to enforce and normalize AI cybersecurity and privacy. It is essential to ensure that ML systems can achieve regulatory compliance and satisfy the standard requirements.
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Toronto, Ontario, CA M3J 1P3
Reference Number:
172-2024-2025-Q4-1026307
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
119306736
Recipient Type:
Academia
Recipient's Legal Name:
York University
Federal Riding Name:
Humber River--Black Creek
Federal Riding Number:
35041
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710