Project Grant 2147276
Award Date
2/15/22
Completion Date
1/31/25
Dollars Obligated
$625K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
College Park, MD 20742, USA
Similar Awards
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...
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 $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 $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 $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 three-year, $292,993 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop fair algorithms for auctions, pricing, and marketing mediated by artificial intelligence. The grantee, Columbia University, will design new theoretical frameworks and AI algorithms to ensure consumers from protected groups are not harmfully discriminated against in advertising auctions, ride-sharing and loan pricing, and targeted social media...
This Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The $289,894 award, effective October 1, 2024, supports Emory University's research project titled "Towards Fairness in the Real World Under Generalization, Privacy and Robustness Challenges." The project aims to investigate and develop solutions to ensure fairness in AI algorithms, which face practical...
The Trustees of the University of Pennsylvania received a $392,992 Project Grant from the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research and education activities aimed at developing machine learning techniques that break through the fairness-accuracy tradeoff paradigm. Specifically, the university researchers will draw on ideas from learning theory and...
The National Science Foundation awarded a $500,000 Project Grant to the Texas A&M Engineering Experiment Station to advance optimization for threshold-agnostic fair artificial intelligence systems under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year project aims to develop scalable stochastic optimization algorithms and novel threshold-agnostic fairness measures to directly optimize machine learning models for fairness without reliance on...
This three-year, $597,194 National Science Foundation project grant supports research at the Toyota Technological Institute at Chicago to advance the foundations of societal machine learning. The goals are to provide guarantees of fairness, accuracy, and positive societal impacts for machine learning systems used in applications impacting people. Key research areas include understanding fairness in machine learning contexts, especially regarding biased training data and multi-stage decisions;...