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Strengthening the Integrity of Government Payments Using Artificial Intelligence
Summary
Tens of billions of taxpayer dollars are lost every year due to improper payments to the federal government. These improper payments arise from agency and claimant errors as well as outright fraud. Data analytics can help identify errors and fraud, but often only identify improper payments after they have already been issued.
Artificial intelligence (AI) in general—and machine learning (ML) in particular (AI/ML)—could substantially improve the accuracy of federal payment systems. The next administration should launch an initiative to integrate AI/ML into federal agencies’ payment processes. As part of this initiative, the federal government should work extensively with non-federal entities—including commercial firms, nonprofits, and academic institutions—to address major enablers and barriers pertaining to applications of AI/ML in federal payment systems. These include the incidence of false positives and negatives, perceived and actual fairness and bias issues, privacy and security concerns, and the use of ML for predicting the likelihood of future errors and fraud.
Congress should foster a more responsive and evidence-based ecosystem for GenAI-powered educational tools, ensuring that they are equitable, effective, and safe for all students.
Without independent research, we do not know if the AI systems that are being deployed today are safe or if they pose widespread risks that have yet to be discovered, including risks to U.S. national security.
Companies that store children’s voice recordings and use them for profit-driven applications without parental consent pose serious privacy threats to children and families.
Privacy laws are only effective if they include civil rights protections that ensure personal data is processed safely and fairly regardless of race, gender, sexuality, age, or other protected characteristics.