Analyzing Public Policy using Artificial Intelligence Assurance at CCI
Project Overview
Our work utilizes big datasets by federal agencies to analyze and evaluate public policy in economics and agricultural trade using AI methods. The applied intelligent methods require extensive assurance to provide validation of the outcomes that are data-driven, trustworthy, unbiased, and explainable to a farmer or a policy maker.
CALS Strategic Priority(ies) that best describes project goals:
Priority 1: Advance excellence in research, teaching and extension for the commonwealth and beyond
Priority 2: Elevate the Ut Prosim (that I may serve) difference
BENEFICIARIES: the federal government, policy analysts, researchers, data scientists, AI engineers and farmers
TOOLS USED: database technologies, SQL, C#, R, Python, CCI AI Testbed, Kubernetes, and Keras
METHODS USED: Regression, Neural Networks, Reinforcement Learning, Genetic Algorithms, Clustering, and Causal inference
ISSUE: Building a data science infrastructure for federal government agencies to promote data openness and the democratization of policy.
WHAT WAS DONE TO ADDRESS THIS ISSUE: The government, being one of the most important data owners, is riding the wave of data and business intelligence. However, federal agencies have certain requirements and bureaucracies for data-related processes, as well as certain rules and specific regulations that would entail special models for building and managing data analytical systems. Methods developed by our team address this issue.
POTENTIAL IMPACTS: AI assurance techniques, governmental data openness, data democracy, transparency and accountability measures, data-driven policy making
CAIA and CCI are collaborating on AI methods for the SmartFarms initiative. AI algorithms can aid farmers in solving challenges on their farm such as crop management, decision making, animal health, and yield maximization. Such deployments provide unparallel testbeds for AI assurance methods.
Project Team:
- Feras A. Batarseh
Associate Professor, Electrical and Computer Engineering
batarseh@vt.edu
https://ece.vt.edu/people/profile/batarseh - Laura Freeman, Department of Statistics
CCI Students
Graduate Students:
- Andrei Svetovidov
- Nazmul Kabir Sikder
- Pei Wang
Undergraduates:
- Dominick Perini
- Madison J. Williams
- Sai Gurrapu