Project Grant 2245796
- This $625,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into end-to-end fairness for algorithm-in-the-loop decision making in the public sector. New York University will lead the development of novel methods for identifying and correcting biases in massive, multivariate data used for algorithmic decision making. This will include building models to represent human decision making processes, auditing tools to...
- The National Science Foundation awarded $235,438 under the Computer and Information Science and Engineering federal grant program to develop interpretable and fair machine learning frameworks, algorithms, and methodologies. The two-year project grant aims to address concerns that machine learning algorithms can inadvertently amplify human biases by funding research to design scalable, data-driven methods with provable fairness guarantees. Specifically, the grant will support the development of...
- This $331,698 National Science Foundation project grant supports research at Michigan State University to develop fair machine learning systems through improved data representations. Specifically, the Computer and Information Science and Engineering grant will fund research to study fundamental tradeoffs between utility and fairness in different data representations. The researchers will identify solutions to reduce gaps in achieving optimal tradeoffs and develop new representations and...
- This National Science Foundation (NSF) Project Grant award to Purdue University, under the Computer and Information Science and Engineering program (CFDA 47.070), focuses on developing novel technologies to enable robust, fair, and explainable data-driven decision-making systems. The $466,411 award, effective July 1, 2023 through June 30, 2028, will fund research to: 1) detect and mitigate biases in machine learning model outcomes, 2) assess the validity of data for learning fair and trustworthy...
- The National Science Foundation (NSF) awarded a $441,404 Computer and Information Science and Engineering (CFDA 47.070) project grant to the University of California, Berkeley (UC Berkeley) to conduct research on improving the fairness and efficiency of medical decision-making, both by human clinicians and algorithms. The project aims to detect bias in medical decisions around allocation of testing, healthcare quality assessment, and interpretation of medical images. It will develop...
- This $600,000 federal Project Grant award from the National Science Foundation (NSF)'s Computer and Information Science and Engineering (CISE) program will support research and education efforts at the University of Illinois on the topics of fairness in algorithmic decision-making and machine learning. The project aims to advance the state-of-the-art on achieving fairness in computational resource allocation tasks, like vaccine distribution, as well as developing fairness concepts for machine...
- 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 $597,149 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance fundamental research in fair algorithmic decision-making. The project at Purdue University will develop novel algorithms and software to facilitate the adoption and evaluation of fair artificial intelligence (AI) systems, with a focus on promoting health equity in applications like Alzheimer's disease research. Key...
- The National Science Foundation awarded $739,500 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to The Trustees of Columbia University in the City of New York for the project "III: TOWARDS CAUSAL FAIR DECISION-MAKING" from May 15, 2021 through April 30, 2024. The funding will support investigator-initiated research and education in computing, communications, and information science and engineering to advance the development and use of...
- The University of Michigan was awarded a $291,999 Project Grant from the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year collaborative research project aims to advance fairness in web database applications through investigator-initiated research. Specifically, the grant will support the University of Michigan and its research partners in examining issues of...
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 will train machine learning models on human decision data with rebalanced demographic distributions, then test the models for predictive bias using comparative judgments. Any bias detected in the trained models will indicate potential unfairness in the original human decisions. This approach aims to isolate algorithmic bias inherited from training on human decisions in order to identify bias in the decision-making process. The results could help advance equity in domains like talent hiring, credit approval, and school admissions where algorithms guide or inform human judgment.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 2/28/23 |