Project Grant 2146091
- 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...
- 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 (NSF) awarded a $582,031 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the Regents of the University of Michigan, doing business as the University of Michigan. The grant will fund a 5-year research project focused on "Achieving Explainable Artificial Intelligence (AI) Through Human-AI Interaction." The goal of the project is to develop new scientific knowledge and design guidelines for delivering...
- This $300,000 EAGER (Early-concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a framework called XAISE (eXplainable Artificial Intelligence for Science and Engineering) to enhance the explainability of artificial intelligence (AI) models for scientific and engineering applications. The project seeks to design, develop, and implement XAISE to improve the explainability of...
- This Project Grant award of $119,764 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The award is for the project "CAREER: CERTIFIED EXPLANATIONS FOR TRUSTWORTHY ARTIFICIAL INTELLIGENCE", which aims to develop formal specifications, verification frameworks, and AI system architectures that can provide provable assurances and certified explanations for the reasoning behind AI...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program aims to develop scalable algorithms and methods for improving the explainability and robustness of Artificial Intelligence (AI) models. The $350,000 grant awarded to the University of California, Berkeley will leverage spectral analysis and coding theory techniques to identify key input features and interactions that drive AI model predictions. This...
- The U.S. National Science Foundation (NSF) awarded a $332,925 project grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Florida International University (FIU). The project, titled "COLLABORATIVE RESEARCH: III: SMALL: AN INFORMATION-THEORETIC FRAMEWORK FOR EXPLAINABLE AND EXPLANATION-ASSISTED GRAPH LEARNING", will develop a comprehensive framework for making graph neural network (GNN) predictions explainable and trustworthy. The research...
- This Project Grant award for $175,000 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The project, titled "Formalizing Human-Interpretable Machine Learning," aims to develop approaches for training artificial intelligence systems, such as self-driving cars, to make their decision-making processes more transparent and interpretable to people. The research will involve pairing...
- The National Science Foundation (NSF) awarded a $175,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to Wake Forest University. The grant, with a period of performance from June 15, 2025 to May 31, 2027, aims to address computational inefficiencies in current explainable AI methods. Key objectives include accelerating computationally demanding interpretation algorithms, constructing unified explainer models using manifold-based...
- This Project Grant award of $150,000 from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program supports research on "Algorithm-Informed Neural Networks (AINNs)". The key objectives are to develop neural network architectures that integrate well-established algorithmic principles to enhance the explainability, reliability, and efficiency of AI systems. By embedding logical steps into AI models, this approach aims...
This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $576,351 to Purdue University to advance fair and explainable artificial intelligence through new algorithms and human-centered approaches. The award supports research activities from August 15, 2022 to July 31, 2027 to develop a family of fair, explainable, and robust data mining algorithms with theoretical foundations that facilitate fair predictions while protecting sensitive information. The research will also design novel explainable and robust models with guarantees on generalization and efficiency, and promote human-in-the-loop interventions to repair incorrect predictions. The work combines rigorous analysis with emerging application problems in responsible data science and aims to ease efforts to build, adopt, and interact with fair machine learning models.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $576.4k | 8/9/22 |