NSF Responsible AI Program for Fairness Accountability Transparency and Ethics Research

NSF Responsible AI Program for Fairness Accountability Transparency and Ethics Research is sponsored by National Science Foundation (NSF). This program supports foundational research addressing the responsible development and deployment of artificial intelligence, focusing on fairness, accountability, transparency, and ethics.

NSF 20-566: NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon | NSF - U.S. National Science Foundation Archived funding opportunity This solicitation is archived. Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements .

All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects. NSF 20-566: NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon (FAI) Download the solicitation (PDF, 0.

9mb) National Science Foundation Directorate for Computer and Information Science and Engineering Division of Information and Intelligent Systems Directorate for Social, Behavioral and Economic Sciences Division of Behavioral and Cognitive Sciences Full Proposal Deadline(s) (due by 5 p. m.

submitter's local time): Important Information And Revision Notes This solicitation is a revision of NSF 19-571 , the solicitation for the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon. Significant changes include the following: Letters of intent are no longer required.

Any proposal submitted in response to this solicitation should be submitted in accordance with the revised NSF Proposal & Award Policies & Procedures Guide (PAPPG) ( NSF 20-1 ), which is effective for proposals submitted, or due, on or after June 1, 2020.

Summary Of Program Requirements NSF Program on Fairness in Artificial Intelligence 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, intended to be shared across all segments of society.

Broad acceptance and adoption 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, 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, potential adverse biases and effects, mitigation strategies, algorithmic advances, fairness objectives, validation of fairness, and advances in broad accessibility and utility. 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.

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. Cognizant Program Officer(s): Please note that the following information is current at the time of publishing.

See program website for any updates to the points of contact.

Todd Leen, Program Director, CISE/IIS, Sylvia Spengler, Program Director, CISE/IIS, Steven Breckler, Program Director, SBE/BCS, Applicable Catalog of Federal Domestic Assistance (CFDA) Number(s): --- Computer and Information Science and Engineering --- Social Behavioral and Economic Sciences Anticipated Type of Award: Standard Grant or Continuing Grant Estimated Number of Awards: 6 to 10 Award Size: $750,000 up to a maximum of $1,250,000 for periods of up to 3 years.

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds. Anticipated Funding Amount: $7,600,000 Who May Submit Proposals: 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 is expected to bring computer science expertise to the research. Limit on Number of Proposals per Organization: There are no restrictions or limits.

Limit on Number of Proposals per PI or Co-PI: 1 An individual may participate in at most one proposal as PI, co-PI, or Senior Personnel. These eligibility constraints will be strictly enforced in order to treat everyone fairly and consistently.

In the event that an individual exceeds this limit, proposals received within the limit will be accepted based on the earliest date and time of proposal submission (i.e., the first proposal received will be accepted and the remainder will be returned without review). This limitation includes proposals submitted by the prime organization and any subawards included as part of a collaborative proposal. No exceptions will be made.

Proposal Preparation and Submission Instructions A. Proposal Preparation Instructions Letters of Intent: Not required Preliminary Proposal Submission: Not required Full Proposals submitted via FastLane: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=pappg . Full Proposals submitted via Research. gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply.

The complete text of the PAPPG is available electronically on the NSF website at: https://www. nsf. gov/publications/pub_summ.

jsp? ods_key=pappg . Full Proposals submitted via Grants.

gov: NSF Grants. gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants. gov guidelines apply (Note: The NSF Grants.

gov Application Guide is available on the Grants. gov website and on the NSF website at: https://www. nsf.

gov/publications/pub_summ. jsp? ods_key=grantsgovguide ).

Cost Sharing Requirements: Inclusion of voluntary committed cost sharing is prohibited. Indirect Cost (F&A) Limitations: Other Budgetary Limitations: Full Proposal Deadline(s) (due by 5 p. m.

submitter's local time): Proposal Review Information Criteria National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information Additional award conditions apply. Please see the full text of this solicitation for further information. Additional reporting requirements apply.

Please see the full text of this solicitation for further information. 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 acceptance and adoption of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends, on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explainability, 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, potential adverse biases and effects, mitigation strategies, validation of fairness, 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 awards. 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.

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.

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 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, and accountability in AI systems; Factors that affect algorithmic trustworthiness; Ethical decision-support and decision-making systems; and Detecting and ameliorating, or designing to prevent, biases in data and algorithms.

Specific technical contributions address programmatic goals could therefore 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 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. (See Section V.

Proposal Preparation Instructions.) The lead PI on each proposal is expected to bring computer science expertise to the research. In considering such systems, proposers are encouraged to be ambitious in formulating their scientific explorations and to consider multiple contexts and disciplinary perspectives 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. Proposers should be aware of other NSF programs.

For example, proposals centered on innovations in differential privacy would be more responsive to NSF's Secure and Trustworthy Cyberspace Program . Proposers must request total project budgets ranging from $750,000 to $1,250,000 for periods of up to 3 years.

Principal Investigator (PI) Meetings Up to two PI Meetings will be held annually, in Washington, DC, and another domestic location, likely Seattle, WA, with participation of at least one PI, co-PI, Senior Personnel, or NSF-approved replacement from each funded project team, along with other representatives from the research community, government, and industry.

As noted in "Budget Preparation Instructions," budgets for all projects must include funding for one or more designated project representatives (PI/co-PI/Senior Personnel or NSF-approved replacement) to attend each PI Meeting during the proposed lifetime of the award.

CISE is committed to enhancing the community's awareness of and overcoming barriers to Broadening Participation in Computing (BPC), and to providing information and resources to PIs so that they can develop interest, skills, and activities in support of BPC at all levels of the CISE community (K-12, undergraduate, graduate, and postgraduate).

All PIs are strongly encouraged to include meaningful BPC plans in the Broader Impacts sections of their submitted proposals. More information can be found on the CISE BPC webpage: https://www. nsf.

gov/cise/bpc . (For those specifically interested in NSF programs on the science of broadening participation, not in the context of an FAI submission, please see the science of broadening participation webpage: https://www. nsf.

gov/funding/pgm_summ. jsp? pims_id=505235 .)

An Additional and Separate Funding Opportunity Researchers may also consider the Amazon Research Awards program as another, separate funding opportunity supporting research in fairness in AI. It is anticipated that 6 to 10 awards will be made, with an award size of $750,000 up to a maximum of $1,250,000, for periods of up to 3 years.

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds. IV. Eligibility Information Who May Submit Proposals: 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 is expected to bring computer science expertise to the research. Limit on Number of Proposals per Organization: There are no restrictions or limits.

Limit on Number of Proposals per PI or Co-PI: 1 An individual may participate in at most one proposal as PI, co-PI, or Senior Personnel. These eligibility constraints will be strictly enforced in order to treat everyone fairly and consistently.

In the event that an individual exceeds this limit, proposals received within the limit will be accepted based on the earliest date and time of proposal submission (i.e., the first proposal received will be accepted and the remainder will be returned without review). This limitation includes proposals submitted by the prime organization and any subawards included as part of a collaborative proposal. No exceptions will be made.

V. Proposal Preparation And Submission Instructions A. Proposal Preparation Instructions Full Proposal Preparation Instructions : Proposers may opt to submit proposals in response to this Program Solicitation via FastLane, Research.

gov, or Grants. gov. Full proposals submitted via FastLane: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf. gov .

Proposers are reminded to identify this program solicitation number in the program solicitation block on the NSF Cover Sheet For Proposal to the National Science Foundation. Compliance with this requirement is critical to determining the relevant proposal processing guidelines. Failure to submit this information may delay processing.

Full Proposals submitted via Research. gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf. gov .

The Prepare New Proposal setup will prompt you for the program solicitation number. Full proposals submitted via Grants. gov: Proposals submitted in response to this program solicitation via Grants.

gov should be prepared and submitted in accordance with the NSF Grants. gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants. gov .

The complete text of the NSF Grants. gov Application Guide is available on the Grants. gov website and on the NSF website at: ( https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=grantsgovguide ). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants. gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button.

Paper copies of the Grants. gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf. gov .

See PAPPG Chapter II. C. 2 for guidance on the required sections of a full research proposal submitted to NSF.

Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions. Multi-Organizational Proposals: For collaborative proposals, the proposal must be submitted by one prime organization with funding for all other participating organizations made through subawards . See PAPPG Chapter II.

D. 3. a for additional information.

The PI on a proposal to be awarded will be asked to provide contact information for the grants administrator for each organization receiving a sub-award. Proposals submitted as separately submitted collaborative proposals (as described under PAPPG Chapter II. D.

3. b) will be returned without review. Proposal titles should begin with the acronym “FAI” followed by a colon and then the title.

For example, “FAI: Descriptive Title”. Project Descriptions are limited to 15 pages in length. Proposals must include all sections required by the PAPPG, including Intellectual Merit, Broader Impacts, and Results from prior NSF support.

In addition to the sections required by the PAPPG (“Intellectual Merit,” “Broader Impacts,” and, if applicable, “Results from Prior NSF Support”), the Project Description must include the following separate sections, clearly labeled with the headings used below (i.e., “Transformative research,” “Embedding innovation in real systems,” and “Evaluation”).

Proposals lacking one or more of these sections will be returned without review. As the central focus of the Project Description, proposals should describe the challenges that drive fundamental computer science research, incorporating multiple disciplinary perspectives as needed and appropriate to the scope of the work.

All proposals should specify the research questions, hypotheses, and challenges that underpin the proposed project and clearly demonstrate its importance to fairness. Embedding innovations in real systems Proposals should describe how the research will include development and testing, pilots, implementations, or deployments in real systems, considering the circumstances and associated challenges with respect to fairness.

Describe approaches to the dissemination of research advances, capabilities, and outcomes, with an emphasis on ensuring broad and timely access to such advances and accelerating their societal impact. Proposals should describe how progress and outcomes will be evaluated. All proposals should specify the measures or metrics to be used in testing hypotheses and evaluating models and deployments.

Describe the methods, measures, and criteria for assessing progress and outcomes appropriate to the proposal.

In the Supplementary Documents Section, upload the following: A list of Project Personnel and Partner Institutions (required) (Note: In collaborative proposals, the prime organization should provide this information for all participants): Provide current, accurate information for all personnel and organizations involved in the project. NSF staff will use this information in the merit review process to manage reviewer selection.

The list must include all PIs, co-PIs, Senior Personnel, paid/unpaid Consultants or Collaborators, Subawardees, Postdoctoral Researchers, and project-level advisory committee members. This list should be numbered and include (in this order) Full name, Organization(s), and Role in the project, with each item separated by a semi-colon. Each person listed should start a new numbered line.

For example: Mary Smith; XYZ University; PI John Jones; University of PQR; Senior Personnel Jane Brown; XYZ University; Postdoctoral Researcher Bob Adams; ABC Community College; Paid Consultant Susan White; DEF Corporation; Unpaid Collaborator Tim Green; ZZZ University; Subawardee Collaboration Plans (required): Since the success of collaborative research efforts are known to depend on thoughtful coordination mechanisms that regularly bring together the various participants of the project, proposals must include a Collaboration Plan of up to 2 pages.

The length of and degree of detail provided in the Collaboration Plan should be commensurate with the complexity of the proposed project and be responsive to the themes outlined in Section V. A. Project Description.

Where appropriate, the Collaboration Plan might include: 1) the specific roles of the project participants in all organizations involved; 2) information on how the project will be managed across all the investigators, organizations, and/or disciplines; 3) identification of the specific coordination mechanisms that will enable cross-sector, cross-investigator, cross-organization, and/or cross-discipline scientific integration (e.g., yearly conferences, graduate student exchange, project meetings at conferences, use of the grid for videoconferences, software repositories, etc.); and 4) specific references to the budget line items that support collaboration and coordination mechanisms.

If a proposal does not include a Collaboration Plan of up to 2 pages, that proposal will be returned without review . Data Management Plan (required): See Chapter II. C.

2. j of the PAPPG for full policy implementation. For additional information on the Dissemination and Sharing of Research Results, see: https://www.

nsf. gov/bfa/dias/policy/dmp. jsp .

For specific guidance for Data Management Plans submitted to the Directorate for Computer and Information Science and Engineering (CISE) see: https://www. nsf. gov/cise/cise_dmp.

jsp . Inclusion of voluntary committed cost sharing is prohibited. Budget Preparation Instructions: Budgets for all projects must include funding for one or more designated project representatives (PI/co-PI/Senior Personnel or NSF- approved replacement) to attend each PI Meeting during the proposed lifetime of the award.

For budget preparation purposes, PIs should assume two meetings will be held annually, one in the Washington, DC, area and one in another domestic location, likely Seattle, WA.

The budget submitted with the proposal should include all necessary project funds without regard to the two funding organizations; NSF and Amazon will inform selected PIs of the breakdown in funding between the two organizations and will request revised budgets at that point. Full Proposal Deadline(s) (due by 5 p. m.

submitter's local time): D. FastLane/Research. gov/Grants.

gov Requirements For Proposals Submitted Via FastLane or Research. gov: To prepare and submit a proposal via FastLane, see detailed technical instructions available at: https://www. fastlane.

nsf. gov/a1/newstan. htm .

To prepare and submit a proposal via Research. gov, see detailed technical instructions available at: https://www. research.

gov/research-portal/appmanager/base/desktop? _nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission. html .

For FastLane or Research. gov user support, call the FastLane and Research. gov Help Desk at 1-800-673-6188 or e-mail fastlane@nsf.

gov or rgov@nsf. gov . The FastLane and Research.

gov Help Desk answers general technical questions related to the use of the FastLane and Research. gov systems. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants. gov: Before using Grants. gov for the first time, each organization must register to create an institutional profile.

Once registered, the applicant's organization can then apply for any federal grant on the Grants. gov website. Comprehensive information about using Grants.

gov is available on the Grants. gov Applicant Resources webpage: https://www. grants.

gov/web/grants/applicants. html . In addition, the NSF Grants.

gov Application Guide (see link in Section V. A) provides instructions regarding the technical preparation of proposals via Grants. gov. For Grants.

gov user support, contact the Grants. gov Contact Center at 1-800-518-4726 or by email: support@grants. gov .

The Grants. gov Contact Center answers general technical questions related to the use of Grants. gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants. gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.

gov. The completed application will be transferred to the NSF FastLane system for further processing. Proposers that submitted via FastLane or Research. gov may use Research.

gov to verify the status of their submission to NSF. For proposers that submitted via Grants. gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.

gov. After proposers have received an e-mail notification from NSF, Research. gov should be used to check the status of an application. VI.

NSF Proposal Processing And Review Procedures Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review.

All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process.

Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional.

Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards.

A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1. A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www. nsf.

gov/bfa/dias/policy/merit_review/ . One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy.

NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

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. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports. A.

Merit Review Principles and Criteria The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education.

To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes."

NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects. 1. Merit Review Principles These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards.

Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply: All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge. NSF projects, in the aggregate, should contribute more broadly to achieving societal goals.

These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project