Project Grant 2340661
- This $400,000 Project Grant awarded by the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) supports research to understand how socioeconomic heterogeneity within the U.S. scientific workforce impacts scientific discovery and innovation. The project aims to (i) create a unique individual-level dataset linking researcher socioeconomic backgrounds to scholarly topics, (ii) make an anonymized version of this dataset publicly available, (iii)...
- The National Science Foundation (NSF) is providing a $299,181 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Colorado to study the mechanisms that lead to a lack of diversity in computer science (CS) research subfields. The three-part study includes in-depth interviews, textual analysis of subfield discourse, and a large-scale survey to quantify participation of domestic racial minorities in CS subfields. The goal is to develop a...
- This National Science Foundation (NSF) Division of Social and Economic Science award, under the CFDA 47.075 Social, Behavioral, and Economic Sciences program, provides $503,800 to the University of Michigan to research using artificial intelligence (AI) and machine learning to improve job matching for low-skilled workers displaced by the COVID-19 pandemic. The 2-year project will use a randomized controlled trial to evaluate whether AI-assisted algorithmic matching of jobseeker skills and...
- This $625,000 National Science Foundation project grant supports research at the University of Maryland, College Park toward developing fair machine learning algorithms and applications. Specifically, the grant funds the "Toward Fair Decision Making and Resource Allocation with Application to AI-Assisted Graduate Admission and Degree Completion" project. The research aims to design artificial intelligence systems that make admission and resource allocation decisions for graduate...
- This two-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations provides $174,999 to develop a novel technique for testing fairness in human decisions using algorithmic bias. The work will be performed by Rochester Institute of Technology under the Computer and Information Science and Engineering program (CFDA 47.070), which supports research and education across computing, communications, and information science. Specifically, the awardee...
- This Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences Directorate (CFDA 47.075) provides $287,320 to the University of Washington from September 1, 2023 to August 31, 2025. The project will develop strategies for collecting and reporting information about the racial identity of research participants in an ethical and respectful manner. The research team plans to analyze participant-provided racial identity data, conduct interviews and focus...
- This $100,000 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support research to explore how technological changes are impacting worker skills and employment. The project will analyze future skill demands driven by technological disruption and develop effective, affordable educational approaches to enable workers, especially those without college degrees, to reskill and upskill. The research aims to leverage...
- Federal Project Grant Award Summary The National Science Foundation (NSF) Division of Social, Behavioral and Economic Science awarded a $180,678 Project Grant to Rice University beginning September 1, 2025 and concluding August 31, 2027 under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). This collaborative research initiative investigates artificial intelligence (AI) chatbot systems used in workplace hiring practices, with specific focus on detecting potential bias in...
- This Project Grant award from the National Science Foundation's (NSF) Division of Information and Intelligent Systems (CFDA 47.070 - Computer and Information Science and Engineering) provides $400,000 to New York University (NYU) from September 1, 2023 to August 31, 2027. The project focuses on developing methods to validate and design responsible algorithmic rankers used in critical domains like hiring, education, and lending. Key objectives include: 1) quantifying the impact of input...
- This $111,984 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support a collaborative research project focused on mapping the state of AI ethics education across diverse higher education institutions in the United States. The project aims to: (a) comprehensively survey current and planned AI ethics education interventions, (b) develop a framework to understand how faculty perceive and conceptualize AI ethics...
CAREER: SKIN COLOR AND INEQUALITY: MEASUREMENT AND MITIGATION -SKIN COLOR DISCRIMINATION, REGARDLESS OF RACE, IS COMMON ACROSS SOCIETIES AROUND THE WORLD. ALSO KNOWN AS COLORISM, IT TYPICALLY MANIFESTS ITSELF IN FAVORING LIGHTER SKIN OVER DARKER SKIN AND HAS BEEN FOUND TO BE AN IMPORTANT PREDICTOR OF MANY OUTCOMES, INCLUDING OCCUPATION, EDUCATIONAL ATTAINMENT, CRIMINAL JUSTICE SYSTEM INVOLVEMENT, AND MARITAL OUTCOMES, AMONG OTHERS. HOWEVER, THERE IS NO OBJECTIVE WAY OF MEASURING SKIN COLOR. THIS CAREER AWARD WILL FUND RESEARCH TO DEVELOP NEW METHODS, BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY, TO ACCURATELY MEASURE SKIN TONE, FOR A LARGE NUMBER OF YOUNG PEOPLE AND MAKE THE DATA AVAILABLE TO OTHER RESEARCHERS. THIS WILL ALLOW MORE RESEARCH ON THE RELATIONSHIP BETWEEN SKIN TONE AND SEVERAL OUTCOMES. THE RESEARCH WILL ALSO USE THE DATA TO INVESTIGATE HOW SKIN TONE AFFECTS WHO IS CHOSEN TO PARTICIPATE IN CRIME INTERVENTION PROGRAMS AS WELL AS PARENTAL INVESTMENTS IN THEIR CHILDREN?S EDUCATION. THE RESULTS OF THIS RESEARCH WILL PROVIDE INPUTS INTO EFFORTS TO REDUCE DIFFERENTIAL OPPORTUNITIES BASED IN SKIN TONE DIFFERENCES. THIS WILL IMPROVE THE OVERALL QUALITY OF THE US LABOR FORCE, INCREASE PRODUCTIVITY, ECONOMIC GROWTH, AND IMPROVE THE WELL-BEING OF CITIZENS. THE RESULTS OF THIS RESEARCH WILL ALSO HELP ESTABLISH THE US AS A GLOBAL LEADER IN REDUCING THE EFFECTS OF COLORISM ON OUTCOMES. DESPITE THE POTENTIAL DISTORTIONARY EFFECTS ON DECISION-MAKING, COLORISM HAS BEEN UNDERSTUDIED IN THE ECONOMICS LITERATURE, PARTLY BECAUSE IT IS DIFFICULT TO MEASURE COLORISM. THE FIRST PART OF THIS CAREER RESEARCH AWARD WILL DEVELOP AI AND NEURAL NETWORKS ASSISTED COMPUTATIONAL PROTOCOLS TO SYSTEMATICALLY CLASSIFY SKIN COLOR THAT IS SCALABLE, REPLICABLE, ACCOUNTS FOR LOCAL KNOWLEDGE IN A STUDY SETTING, AND USE THE PROTOCOLS TO CREATE A LARGE DATA SET ON SKIN COLOR. THE METHODS AND DATA COLLECTED WILL OPEN NEW AVENUES OF INQUIRY ACROSS THE SOCIAL SCIENCES, MEDICINE, HUMANITIES, AND LAW. THE RESEARCH PROJECT WILL ALSO STUDY HOW INDIVIDUAL-LEVEL INTERVENTIONS, SUCH AS THOSE RELATED TO EDUCATION AND OTHER HUMAN CAPITAL INVESTMENTS, AND EMPOWERMENT PROGRAMS, MAY BE CAUSALLY INFLUENCED BY COLORISM. THIS PART OF THIS RESEARCH WILL ALSO STUDY THE HETEROGENEOUS IMPACTS OF INTERVENTIONS BASED ON SKIN COLOR, AS WELL AS EXPLORE POTENTIAL UNDERLYING MECHANISMS THAT CONTRIBUTE TO THESE DIFFERENTIAL EFFECTS. THE RESULTS OF THIS RESEARCH WILL PROVIDE INPUTS INTO EFFORTS TO REDUCE DIFFERENTIAL OPPORTUNITIES TO PEOPLE BASED ON SKIN COLOR; EFFORTS THAT ARE LIKELY TO IMPROVE THE OVERALL QUALITY OF THE US LABOR FORCE, INCREASE PRODUCTIVITY, ECONOMICS GROWTH, AND THE WELL-BEING OF AMERICAN CITIZENS. THE RESULTS OF THIS RESEARCH WILL ALSO HELP ESTABLISH THE US AS THE GLOBAL LEADER IN REDUCING THE EFFECTS OF COLORISM ON MANY OUTCOMES. 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.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 5/11/26 | ||
| Not listed | $171.1k | 9/9/24 | ||
| Not listed | $150.0k | 6/18/24 |