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Source: IRS Form 990 via ProPublica Nonprofit Explorer
Total Revenue
▼$5.9M
Total Contributions
$5.8M
Total Expenses
▼$5.5M
Total Assets
$10.4M
Total Liabilities
▼$184.2K
Net Assets
$10.2M
Officer Compensation
→$405.8K
Other Salaries
$3.4M
Investment Income
▼$46.7K
Fundraising
▼$0
Source: USAspending.gov · Searched by organization name
Total Federal Funding
$1.4M
Awards Found
4
| Awarding Agency | Description | Amount | Fiscal Year | Period |
|---|---|---|---|---|
| National Science Foundation | CHS: LARGE: COLLABORATIVE RESEARCH: PERVASIVE DATA ETHICS FOR COMPUTATIONAL RESEARCH | $457K | FY2017 | Sep 2017 – Aug 2021 |
| National Science Foundation | REDDDOT PHASE 1: PLANNING GRANT: ASSESSING ENVIRONMENTAL IMPACTS OF AI THROUGH PARTICIPATORY METHODS -THIS PROJECT SEEKS TO HOLISTICALLY MEASURE AND UNDERSTAND THE SOCIAL AND ENVIRONMENTAL IMPACTS OF ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES, PARTICULARLY CONCERNING THE MASSIVE DATA CENTERS AND OTHER INFRASTRUCTURES THAT MUST BE BUILT TO SUPPORT THEM. WHILE MANY AI DEVELOPERS HAVE BEGUN MEASURING THE CARBON COSTS OF THEIR PROJECTS, COMPARATIVELY LITTLE IS KNOWN ABOUT HOW PEOPLE EXPERIENCE THIS ASPECT OF AI DEVELOPMENT?S IMPACTS ON THE PHYSICAL ENVIRONMENT AND COMMUNITY LIFE. BY DRAWING ON ESTABLISHED METHODS FOR PARTICIPATORY RESEARCH, THIS PROJECT AIM TO ENGAGE IMPACTED COMMUNITIES IN THE PROCESS OF DEFINING AND MITIGATING THOSE IMPACTS. THE PROJECT AIMS TO ENSURE THAT CONCERNS ABOUT LAND, ENERGY, AND WATER USE, AND QUALITY OF LIFE ARE ADEQUATELY ACCOUNTED FOR AS GOVERNMENTS AND COMPANIES SEEK MORE OPPORTUNITIES TO EXPAND THE AI ECONOMY. THIS PLANNING GRANT WILL CREATE A HUB AS A CONTAINER FOR CO-DESIGNING FRAMEWORKS FOR ASSESSING BOTH AI?S ENVIRONMENTAL IMPACTS AND THE SUCCESS OF ITS APPLICATION TO PARTICULAR ENVIRONMENTAL CASE STUDIES. USING PARTICIPATORY METHODS, THE PROJECT EXAMINES THE COMPLEX, SOCIOTECHNICAL WAYS THAT AI IS IMPACTING ECOSYSTEMS AND COMMUNITIES. THE ARTIFICIAL INTELLIGENCE ENVIRONMENTAL IMPACTS ACT OF 2024 CALLS FOR MORE EMPIRICAL STUDIES OF AI?S EFFECTS ACROSS THE LIFECYCLE. MEASURING AND ADDRESSING THESE IMPACTS REQUIRES A GROUND-UP ASSESSMENT, ENGAGING THE COMMUNITIES WHO ARE ALREADY LIVING WITH THE DOWNSTREAM EFFECTS OF AI PRODUCTION, USE, AND DISPOSAL. THIS COLLABORATIVE PROJECT AMONG ACADEMIC RESEARCH INSTITUTES, DEVELOPERS, AND NONPROFITS WILL PROVIDE A PROOF-OF-CONCEPT FOR MEASURING, REPORTING, AND MITIGATING AI?S BROAD SPECTRUM OF SOCIOTECHNICAL ENVIRONMENTAL IMPACTS. WORKING WITH ENVIRONMENTAL GROUPS AND THE COMMUNITIES LIKELY TO BE AFFECTED BY DATA CENTERS, E-WASTE, AND OTHER AI-RELATED ASPECTS, THE INTERDISCIPLINARY PROJECT TEAM WILL CONDUCT STAKEHOLDER INTERVIEWS, WORKSHOPS, AND PILOT STUDIES TO INCLUDE COMMUNITY PERSPECTIVES AND ON-THE-GROUND KNOWLEDGE IN FRAMEWORKS FOR EVALUATING THE ENVIRONMENTAL AND SOCIAL IMPACTS OF AI. SUCCESSFUL OUTCOMES OF THIS PLANNING GRANT WILL INFORM PURSUIT OF A FUTURE LONG-TERM ENGAGEMENT BASED ON COLLABORATIONS ESTABLISHED THROUGH OUR NETWORKING EFFORTS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD. | $300K | FY2025 | Oct 2024 – Sep 2026 |
| National Science Foundation | EAGER: COUNCIL FOR BIG DATA, ETHICS, AND SOCIETY | $299.8K | FY2014 | Apr 2014 – Mar 2016 |
| National Science Foundation | BIGDATA: COLLABORATIVE RESEARCH: F: ALGORITHMIC FAIRNESS: A SYSTEMIC AND FOUNDATIONAL TREATMENT OF NONDISCRIMINATORY DATA MINING | $296.6K | FY2016 | Sep 2016 – Aug 2019 |
National Science Foundation
$457K
CHS: LARGE: COLLABORATIVE RESEARCH: PERVASIVE DATA ETHICS FOR COMPUTATIONAL RESEARCH
National Science Foundation
$300K
REDDDOT PHASE 1: PLANNING GRANT: ASSESSING ENVIRONMENTAL IMPACTS OF AI THROUGH PARTICIPATORY METHODS -THIS PROJECT SEEKS TO HOLISTICALLY MEASURE AND UNDERSTAND THE SOCIAL AND ENVIRONMENTAL IMPACTS OF ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES, PARTICULARLY CONCERNING THE MASSIVE DATA CENTERS AND OTHER INFRASTRUCTURES THAT MUST BE BUILT TO SUPPORT THEM. WHILE MANY AI DEVELOPERS HAVE BEGUN MEASURING THE CARBON COSTS OF THEIR PROJECTS, COMPARATIVELY LITTLE IS KNOWN ABOUT HOW PEOPLE EXPERIENCE THIS ASPECT OF AI DEVELOPMENT?S IMPACTS ON THE PHYSICAL ENVIRONMENT AND COMMUNITY LIFE. BY DRAWING ON ESTABLISHED METHODS FOR PARTICIPATORY RESEARCH, THIS PROJECT AIM TO ENGAGE IMPACTED COMMUNITIES IN THE PROCESS OF DEFINING AND MITIGATING THOSE IMPACTS. THE PROJECT AIMS TO ENSURE THAT CONCERNS ABOUT LAND, ENERGY, AND WATER USE, AND QUALITY OF LIFE ARE ADEQUATELY ACCOUNTED FOR AS GOVERNMENTS AND COMPANIES SEEK MORE OPPORTUNITIES TO EXPAND THE AI ECONOMY. THIS PLANNING GRANT WILL CREATE A HUB AS A CONTAINER FOR CO-DESIGNING FRAMEWORKS FOR ASSESSING BOTH AI?S ENVIRONMENTAL IMPACTS AND THE SUCCESS OF ITS APPLICATION TO PARTICULAR ENVIRONMENTAL CASE STUDIES. USING PARTICIPATORY METHODS, THE PROJECT EXAMINES THE COMPLEX, SOCIOTECHNICAL WAYS THAT AI IS IMPACTING ECOSYSTEMS AND COMMUNITIES. THE ARTIFICIAL INTELLIGENCE ENVIRONMENTAL IMPACTS ACT OF 2024 CALLS FOR MORE EMPIRICAL STUDIES OF AI?S EFFECTS ACROSS THE LIFECYCLE. MEASURING AND ADDRESSING THESE IMPACTS REQUIRES A GROUND-UP ASSESSMENT, ENGAGING THE COMMUNITIES WHO ARE ALREADY LIVING WITH THE DOWNSTREAM EFFECTS OF AI PRODUCTION, USE, AND DISPOSAL. THIS COLLABORATIVE PROJECT AMONG ACADEMIC RESEARCH INSTITUTES, DEVELOPERS, AND NONPROFITS WILL PROVIDE A PROOF-OF-CONCEPT FOR MEASURING, REPORTING, AND MITIGATING AI?S BROAD SPECTRUM OF SOCIOTECHNICAL ENVIRONMENTAL IMPACTS. WORKING WITH ENVIRONMENTAL GROUPS AND THE COMMUNITIES LIKELY TO BE AFFECTED BY DATA CENTERS, E-WASTE, AND OTHER AI-RELATED ASPECTS, THE INTERDISCIPLINARY PROJECT TEAM WILL CONDUCT STAKEHOLDER INTERVIEWS, WORKSHOPS, AND PILOT STUDIES TO INCLUDE COMMUNITY PERSPECTIVES AND ON-THE-GROUND KNOWLEDGE IN FRAMEWORKS FOR EVALUATING THE ENVIRONMENTAL AND SOCIAL IMPACTS OF AI. SUCCESSFUL OUTCOMES OF THIS PLANNING GRANT WILL INFORM PURSUIT OF A FUTURE LONG-TERM ENGAGEMENT BASED ON COLLABORATIONS ESTABLISHED THROUGH OUR NETWORKING EFFORTS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
National Science Foundation
$299.8K
EAGER: COUNCIL FOR BIG DATA, ETHICS, AND SOCIETY
National Science Foundation
$296.6K
BIGDATA: COLLABORATIVE RESEARCH: F: ALGORITHMIC FAIRNESS: A SYSTEMIC AND FOUNDATIONAL TREATMENT OF NONDISCRIMINATORY DATA MINING
Source: Federal Audit Clearinghouse (fac.gov)
No federal single audit records found for this organization.
Single audits are required for entities expending $750,000+ in federal awards annually.
Source: IRS e-Filed Form 990
No officer or director compensation data available for this organization.
This data is sourced from IRS Form 990, Part VII. It may not be available if the organization files Form 990-N (e-Postcard) or has not yet been enriched.
Source: IRS Publication 78, Auto-Revocation List & e-Postcard Data
Tax-deductible contributions: Yes
Deductibility code: PC
Sources: IRS e-Filed Form 990 (XML) & ProPublica Nonprofit Explorer
Scroll →
| Year | Revenue | Contributions | Expenses | Assets | Net Assets |
|---|---|---|---|---|---|
| 2023 | $5.9M | $5.8M | $5.5M | $10.4M | $10.2M |
| 2022 | $3.3M | $3.3M | $5.3M | $10.6M | $9.8M |
| 2021 | $6.2M | $6.2M | $4.7M | $13.1M | $11.8M |
| 2020 | $6.5M | $6.4M | $5.7M | $11M | $10.3M |
Sources: ProPublica Nonprofit Explorer & IRS e-File Index
Financial data: IRS Form 990 via ProPublica Nonprofit Explorer (Tax Year 2023)
Federal grants: USAspending.gov (live)
Organization info: IRS Business Master File · ProPublica Nonprofit Explorer
Tax-deductibility: IRS Publication 78
| 2019 | $6.9M | $6.8M | $5.7M | $9.6M | $9.5M |
| 2018 | $7.2M | $7.2M | $5.2M | $8.4M | $8.3M |
| 2017 | $4.8M | $4.8M | $4M | $4.9M | $4.8M |
| 2016 | $4.9M | $4.9M | $3.7M | $4.2M | $4M |
| 2015 | $3.4M | $3.4M | $1.8M | $2.9M | $2.8M |
| 2014 | $1.4M | $1.4M | $210.6K | $1.2M | $1.2M |
| 2021 | 990 | Data |
| 2020 | 990 | Data |
| 2019 | 990 | Data |
| 2018 | 990 | Data |
| 2017 | 990 | Data |
| 2016 | 990 | Data |
| 2015 | 990 | Data |
| 2014 | 990 | Data |