Project Grant 2239824
- This $300,000 Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program aims to investigate algorithmic biases in artificial intelligence (AI)-based ergonomic assessments to ensure the ethical design and deployment of responsible and explainable AI technologies. The key objectives are to: (1) obtain stakeholder perspectives on AI-based ergonomic assessments, (2) identify sources of algorithmic biases, (3) develop and evaluate...
- 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 Project Grant award from the National Science Foundation's (NSF) Division of Information and Intelligent Systems (CFDA 47.070 - Computer and Information Science and Engineering) provides $400,000 to New York University (NYU) from September 1, 2023 to August 31, 2027. The project focuses on developing methods to validate and design responsible algorithmic rankers used in critical domains like hiring, education, and lending. Key objectives include: 1) quantifying the impact of input...
- 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 $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 $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...
- 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 CAREER award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund a $321,061 research project at the University of Chicago. The project aims to develop new AI-powered methods to accurately measure skin tone and investigate how skin tone impacts outcomes like educational attainment, criminal justice involvement, and parental investments. The research will create a large dataset on skin color that will enable new...
- This $721,815 Project Grant award from the National Science Foundation (NSF) Office of Emerging Frontiers and Multidisciplinary Activities aims to support HR specialists in the technology and retail industries in using automated decision systems (ADS) more effectively and accountably for talent acquisition. The project, titled "COLLABORATIVE RESEARCH: FW-HTF-RL: TRAPEZE: RESPONSIBLE AI-ASSISTED TALENT ACQUISITION FOR HR SPECIALISTS", will develop the TRAPEZE framework to empower HR...
- 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...
CAREER: ADVANCING EQUITY IN SELECTION PROBLEMS THROUGH BIAS-AWARE OPTIMIZATION -THIS FACULTY EARLY CAREER DEVELOPMENT PROGRAM (CAREER) GRANT WILL CONTRIBUTE TO THE ADVANCEMENT OF NATIONAL HEALTH, PROSPERITY, AND WELFARE BY DEVELOPING SYSTEMATIC APPROACHES TO REDUCING WORKFORCE INEQUALITY DUE TO IMPLICIT BIAS. WIDELY USED AUTOMATED APPLICANT SCREENING TECHNOLOGIES BRING HIGH RISK OF SYSTEMATICALLY SCREENING-OUT STARS (SKILLED WORKERS TRAINED BY ALTERNATE ROUTES), WHILE IMPOSING QUOTAS ON THE SELECTION OF CANDIDATES FROM DIFFERENT BACKGROUNDS CAN VIOLATE ANTI-DISCRIMINATION LAWS. THIS AWARD SUPPORTS THE DEVELOPMENT OF FUNDAMENTAL METHODOLOGIES FOR TRANSPARENTLY HANDLING CONTEXTUAL BIASES IN CANDIDATE EVALUATION DATA WITHOUT RESORTING TO QUOTAS OR FAIRNESS CONSTRAINTS (DUE TO LEGAL REQUIREMENTS). THIS INTERDISCIPLINARY RESEARCH WILL PROVIDE TOOLS FOR PRACTITIONERS AND POLICYMAKERS TO UNDERSTAND THE INEFFICIENCIES IN THE SYSTEM, LEADING TO A SYNERGISTIC DESIGN OF POLICIES FOR HIRING AND COLLEGE ADMISSIONS. THE ACCOMPANYING EDUCATIONAL PLAN AIMS TO DEVELOP STEAM (STEM+ART) WORKSHOPS FOR HIGH SCHOOL STUDENTS TO UNDERSTAND BIASES IN DATA THROUGH A ?HIRING MANAGER? SIMULATION GAME, A WORKSHOP GEARED TOWARDS POLICY AND LAW PROFESSIONALS, THE DESIGN OF COURSES IN ETHICAL OR, AND THE CONTINUED MENTORSHIP OF STUDENTS WITH A FOCUS ON STEM MINORITIES. THIS RESEARCH WILL DEVELOP FUNDAMENTAL METHODOLOGIES TO MODEL VARIABILITY IN DATA DUE TO ITS CONTEXT, BY USING COUNTERFACTUAL AND CAUSAL ANALYSIS TO CONSTRUCT CARDINAL AND ORDINAL VARIABILITY SETS FOR CANDIDATES? EVALUATION DATA, YIELDING BILINEAR OPTIMIZATION PROBLEMS AND ORDINAL COMBINATORIAL OPTIMIZATION RESPECTIVELY. THE RESEARCH WILL DEVELOP NEW TECHNIQUES TO ADDRESS THESE CHALLENGING PROBLEM CLASSES USING PARAMETRIC OPTIMIZATION AND ORDER THEORY AS A START AND FIND TRACTABLE SOLUTIONS. THIS WORK WILL PROVIDE A FUNDAMENTAL SHIFT IN HOW WE PROCESS CONTEXTUAL DATA, WHILE ADVANCING THE THEORIES OF ORDINAL, ROBUST, PARAMETRIC, AND GENERAL DISCRETE OPTIMIZATION. THE WORK WILL ADDRESS IMPORTANT POLICY-DESIGN QUESTIONS RELATED TO EQUITY-EFFICIENCY TRADE-OFFS, E.G., THE IMPACT OF BIAS-AWARE TECHNIQUES ON EQUITY, DIVERSITY, AND FAIRNESS WHEN DATA IS CONTEXTUAL, THE IMPACT OF CHANGING RISK PARAMETERS IN THE CONSTRUCTION OF VARIABILITY SETS ON CANDIDATE SELECTION AND HIGHLIGHT WAYS TO USE LIMITED RESOURCES TO REDUCE VARIABILITY. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 5/16/25 | ||
| Not listed | $531.9k | 7/5/23 |