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NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon (FAI)

Grants to USA IHEs and Nonprofit Organizations
for Research to Improve Machine Learning

Agency Type:


Funding Source:

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National Science Foundation (NSF) and Amazon

Conf. Date:


Deadline Date:

08/03/21 5:00 PM Submitter's Local Time


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Grants to USA IHEs and nonprofit organizations such as museums, research labs, and observatories for research to improve machine learning and artificial intelligence. Applicants are advised that required registrations may take several weeks to complete. Funding is intended to research methods to assure fairness in AI and ML.

This program will support approaches to AI fundamentals, system development and deployment that ensure benefits are broadly available across all segments of society. To achieve this objective, technologies need to be accepted across groups that differ by factors such as sociocultural identities, age, gender, health status, geography, income, and education. What individuals or groups consider acceptable, and how individuals or groups evaluate trustworthiness, are important for design, evaluation, and deployment. More broadly, methods to assure fairness need to be developed so that, when such methods are applied, they ensure acceptance, trustworthiness, and lack of adverse biases, including social bias.

AI, including ML, raises significant novel challenges around ensuring non-discrimination, due process, and explainability of decision making. Explaining the inference processes of modern ML approaches and algorithms can contribute to a better understanding of the mechanisms that affect fairness, and trust in the algorithms. Likewise, biases introduced by data sampling, data selection, algorithm design, and optimization criteria affect fairness. Hence, research that advances understanding of these factors is encouraged.

This program will support research in a broad array of topics responsive to the overall goals, including but not limited to:

  • Designing fairness into AI systems;
  • Transparency, explainability, inclusivity, and accountability in AI systems;
  • Factors that affect algorithmic trustworthiness;
  • Ethical decision-support and decision-making systems;
  • Detecting and ameliorating, or designing to prevent, biases in data and algorithms; and
  • Ensuring that AI systems are robustly impartial and broadly inclusive.

Specific technical contributions that address programmatic goals might include, but are not limited to:

  • Algorithms and representations that can quickly and appropriately adjust to differences in training and test data arising from different population subgroups;
  • Theoretical limitations on fairness, for example, proving algorithmically that certain fairness criteria are mutually inconsistent;
  • Device interface design approaches that improve human interpretability of technologies and outcomes;
  • Approaches that design-in socio-cultural considerations a priori, thereby enhancing trustworthiness and broad accessibility and utility for emerging AI and ML advances;
  • Algorithmic design choices impacting fairness outcomes in speech, vision, and language applications;
  • Metrics and methods for designing, piloting, and evaluating systems that mitigate against adverse biases, including social bias, and ensure fairness, including the use of human-machine collaboration and decision support; and
  • Statistical methods for detecting bias in systems as they are operating.

This program supports the conduct of fundamental computer science research into theories, techniques, and methodologies that go well beyond today's capabilities. The research must: (1) be transformative, (2) embed innovations in real systems, and (3) include strong evaluation plans. The lead PI on each proposal must bring computer science expertise to the research. Computationally focused research efforts informed by socio-technical and social behavioral needs of the field are broadly encouraged. In considering such systems, proposers are encouraged to be ambitious in formulating their scientific explorations and to consider multiple contexts and to integrate disciplinary perspectives from the social, behavioral, and cognitive science (for example) as needed and appropriate to the scope of the work.

All application areas are encouraged, including but not limited to, speech, language, computer vision, logistics, educational technologies, decision support and decision making. Projects must clearly be driven by fairness considerations and show computational innovation.

GrantWatch ID#:

GrantWatch ID#: 196473

Estimated Total Program Funding:


Number of Grants:

10 to 12

Estimated Size of Grant:

Award Size: $600,000 up to a maximum of $1,000,000

Term of Contract:

Awards will be made for periods of up to 3 years.

Additional Eligibility Criteria:

Proposals may only be submitted by the following:
- 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.
- 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.

The lead PI on each proposal must bring computer science expertise to the research. Computationally focused research efforts informed by socio-technical and social behavioral needs of the field are broadly encouraged.

Individuals affiliated with Amazon may participate in proposals subject to certain limitations and allowances. These limitations and allowances apply to individuals who are currently employed by, consulting for, or on an active agreement to provide services for Amazon. Specifically:
- Such individuals may not participate in their capacity with Amazon.
- Such individuals may participate if they (i) hold a primary appointment at another organization (e.g., a primary academic appointment at an institution of higher education), as applicable to and defined by that organization, and (ii) do so strictly in their capacity at that other organization.

An individual may participate in at most one proposal as PI, co-PI, or Senior Personnel.

Pre-proposal Conference:

FAI Program Webinar- July 6th, 2021

Webinar material located here:

Pre-Application Information:

Full Proposal Deadline: August 03, 2021 by 5:00 PM submitter's local time

Proposers may opt to submit proposals in response to this Program Solicitation via FastLane,, or

NSF Proposal and Award Policies and Procedures Guide:

View the program announcement online:

View this opportunity on

For a list of relevant application documents, go to the Package tab (press Preview) on the link above.

Contact Information:

Before starting your grant application, please review the funding source's website listed below for updates/changes/addendums/conferences/LOIs.

Todd Leen, Program Director, CISE/IIS
telephone: (703) 292-8930

Sylvia Spengler, Program Director, CISE/IIS
telephone: (703) 292-8930

Steven Breckler, Program Director, SBE/BCS
telephone: (703) 292-7369

For FastLane or user support, call the FastLane and Help Desk at 1-800-673-6188 or e-mail or

For user support, contact the Contact Center at 1-800-518-4726 or by email:

CFDA Number:

47.070, 47.075

Funding or Pin Number:


URL for Full Text (RFP):

Geographic Focus:

USA: Alabama;   Alaska;   Arizona;   Arkansas;   California;   Colorado;   Connecticut;   Delaware;   Florida;   Georgia;   Hawaii;   Idaho;   Illinois;   Indiana;   Iowa;   Kansas;   Kentucky;   Louisiana;   Maine;   Maryland;   Massachusetts;   Michigan;   Minnesota;   Mississippi;   Missouri;   Montana;   Nebraska;   Nevada;   New Hampshire;   New Jersey;   New Mexico;   New York City;   New York;   North Carolina;   North Dakota;   Ohio;   Oklahoma;   Oregon;   Pennsylvania;   Rhode Island;   South Carolina;   South Dakota;   Tennessee;   Texas;   Utah;   Vermont;   Virginia;   Washington, DC;   Washington;   West Virginia;   Wisconsin;   Wyoming

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