Project Grant 1633400

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 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems (CFDA 47.070 Computer and Information Science and Engineering) provides $300,000 over 3 years to Carnegie Mellon University (CMU) to develop fair outlier detection algorithms. The project aims to encode fairness into data mining and deep learning-based outlier detection methods, which are commonly used in applications like fraud detection, surveillance, and policing. The key objectives...

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 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 National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE) program supports collaborative research to address issues of privacy and fairness in critical decision-making processes that leverage government data releases. The $169,540 award to the University of Virginia (UVA) aims to: 1) identify and understand biases and fairness issues that may arise when using differentially private data in downstream decision processes; 2) develop...

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 $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 $755,098 under the Computer and Information Science and Engineering federal grant program to the University of Pittsburgh to advance deep learning methods towards spatial fairness from June 2022 to May 2025. The University will develop new statistical formulations and machine learning frameworks to explicitly preserve spatial fairness when applying artificial intelligence to geospatial problems. It will investigate challenges in partitioning spatial data...

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;...