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Fairness in Artificial Intelligence in Collaboration with Amazon (FAI) Program Webinar

July 6, 2021 2:00 PM  to 
July 6, 2021 3:00 PM
Virtual Meeting

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Synopsis of Program:

NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness, intended to be shared across all segments of society. Broad societal acceptance of large-scale deployments of AI systems rely critically on their trustworthiness which, in turn, depends on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explainability, impartiality, inclusivity, and accountability of such systems. For example, the behavior of algorithms for face recognition, speech, and language, especially when integrated into decision support systems applied across different segments of society, would benefit from new foundational research in fairness of AI systems.

NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, inclusivity, potential adverse biases (including social biases) and effects, mitigation strategies, algorithmic advances, fairness objectives, validation of fairness, participatory design, and advances in broad accessibility and utility. Funded projects will enable broadened acceptance of AI systems, helping the U.S. to further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

Advancing AI is a highly interdisciplinary endeavor drawing on fields such as computer science, information science, engineering, statistics, mathematics, cognitive science, psychology, sociology, decision science, and economics. Considerations of practice, often derived from the social, behavioral, and economic sciences, can inform new directions for computational science to better realize the benefits of algorithmic and data fairness. As such, NSF and Amazon expect these varied perspectives to be critical for the study of fairness in AI. NSF's ability to bring together multiple scientific disciplines uniquely positions the agency in this collaboration, while building AI that is fair and unbiased is an important aspect of Amazon's AI initiatives. This program supports the conduct of fundamental computer science research into theories, techniques, and methodologies that go well beyond today's capabilities and are motivated by challenges and requirements in real systems.

NSF’s mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. Consistent with this principle of diversity and particularly suitable for the thrust of this program, NSF and Amazon encourage proposals (either independently or in multi-institution collaborations) from investigators at institutions that serve groups historically underrepresented in STEM disciplines.

 

Register here for this webinar:

https://nsf.zoomgov.com/webinar/register/WN_TE-4nNlVT4OpcR3SrLHWbA

 

Or an H.323/SIP room system:

    H.323: 161.199.138.10 (US West) or 161.199.136.10 (US East)

    Meeting ID: 160 809 7173

    Passcode: 068984

    SIP: 1608097173@sip.zoomgov.com

   Passcode: 068984

 

Live captioning information:

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This event is part of Webinars/Webcasts.

Meeting Type
Webcast

Contacts
Todd Leen, (703) 292-8930, email: tleen@nsf.gov
Sylvia J. Spengler, (703) 292-8930, email: sspengle@nsf.gov
Steven James Breckler, (703) 292-7369, email: sbreckle@nsf.gov
Wendy Nilsen, (703) 292-2568, email: wnilsen@nsf.gov

NSF Related Organizations
Directorate for Computer and Information Science and Engineering

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