NSF Program on Fairness in Artificial Intelligence (AI) in Collaboration with Amazon
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.
At the same time, broad acceptance of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends upon the collective ability to ensure, assess, and ultimately demonstrate the fairness, transparency, explainability, and accountability of such systems.
Importantly, the beneficial effects of AI systems should be broadly available across all segments of society.
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, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity.
Funded projects will enable broadened acceptance of AI systems, helping the U. S. 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, and psychology.
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.
The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness.
At the same time, broad acceptance of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends upon the collective ability to ensure, assess, and ultimately demonstrate the fairness, transparency, explainability, and accountability of such systems.
Importantly, the beneficial effects of AI systems should be broadly available across all segments of society.
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, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity.
Funded projects will enable broadened acceptance of AI systems, helping the U. S. 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, and psychology.
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.
Obtain Full Opportunity Text:
NSF Publication 19-571
Additional Information of Eligibility:
*Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research labs, professional societies and similar organizations in the U. S. 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.
Full Opportunity Web Address:
http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf19571
Contact:
Agency Email Description:
If you have any problems linking to this funding announcement, please contact
Agency Email:
Date Posted:
2019-03-26
Application Due Date:
Archive Date:
2019-07-25
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