Project Grant 2312775

Award Date 10/1/23
Completion Date 9/30/26
Dollars Obligated $450K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Seattle, WA 98195, USA
Similar Awards
This $346,500 three-year Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund research at The Ohio State University examining the long-term impacts of fair machine learning under strategic individual behavior. The researchers will establish an analytical framework to characterize complex sequential interactions between individuals and machine learning systems over repeated interactions. This framework aims to enable...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program totaling $599,996 will fund research into the development of learning-augmented mechanisms from October 1, 2022 to September 30, 2025. The goal of the research is to extend the framework of algorithms with machine-learned predictions to the design of mechanisms in the presence of strategic agents. Specifically, the award recipient Drexel University will consider the design and...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $470,670 to the University of Wisconsin-Madison to investigate the incentives and mechanisms that influence participation in data sharing and collaborative machine learning. The project aims to create protocols that can balance responsibilities and benefits among organizations, while preventing strategic behavior that could undermine data...
The National Science Foundation (NSF) awarded a $1,182,881 Project Grant to the University of California, San Diego (UCSD) under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of new methods to ensure that machine learning models assigned to decisions such as lending and hiring can be changed through individual actions, protecting the right to access these services. The project will create techniques for (1) detecting...
This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to understand the effects of large language models (LLMs) on the work of online information professionals. The $370,692 award to the University of Washington will fund research to develop an epistemological framework for characterizing the risks posed by LLM-generated content, as well as proactive and reactive approaches to assessing...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $599,986 to Stanford University for research titled "Machine Learning with Behavioral and Social Data." The five-year award beginning in August 2022 will support the development of new machine learning algorithms that model human decision-making descriptively based on behavioral data. The researcher aims to build on recent advances in modeling choices as driven by...
This $599,974 Project Grant was awarded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Arizona (Tucson, AZ). The purpose of this 3-year award is to leverage big data and machine learning to improve the efficiency and efficacy of online labor markets (OLMs). The project will focus on three key aims: 1) developing novel technical solutions for...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) in the amount of $395,059 will support research at the University of Washington to develop techniques for encoding societal values into social media ranking algorithms. The key objectives are: Introduce a novel method using large language models to build algorithmic objective functions that represent social science constructs, referred to...
This $253,472 National Science Foundation project grant supports research at The Ohio State University from November 2022 through September 2025 under the Computer and Information Science and Engineering program. The university will conduct a collaborative research project to rigorously model individual strategic behavior and its long-term impacts on the development of fair machine learning techniques. Specifically, the researchers will establish an analytical framework to characterize...
The National Science Foundation Division of Computing and Communication Foundations awarded Oberlin College $239,830 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research from October 1, 2022 to September 30, 2025. The award will fund the development of a new theory of computational learning adapted to economic environments where participants may strategically manipulate data. Specifically, the research seeks to design algorithms...

This National Science Foundation (NSF) Division of Computing and Communication Foundations award, under the CFDA program "Computer and Information Science and Engineering," provides $449,932 to the University of Washington to conduct research on the dynamics, competition, and interventions in machine learning (ML)-enabled markets.

The key focus of the 3-year project is to develop the theoretical and algorithmic foundations for characterizing and shaping ML-enabled market conditions to achieve improved social outcomes. The research will analyze the complex interactions between service providers (who use ML predictions to offer services) and individuals (who choose services based on prediction quality). The project aims to design algorithmic interventions that can enhance metrics like social welfare and fairness in these ML-enabled markets. The research will leverage expertise in game theory, statistical learning, dynamical systems, and optimization to address challenges posed by nonlinear dynamics and nonconvex landscapes in these systems.

Generated 5/14/24, 9:33 AM