
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.
While the U.S. has made significant advancements and remained a global leader in biotechnology over the past decade, the next four years will be critical in determining whether it can sustain that leadership.
It’s paramount to balance both innovation capabilities and risk as we work towards ensuring that the U.S. bioeconomy is a priority area for both the Nation and for National Security.
The Federation of American Scientists supports the National Security Commission on Emerging Biotechnology’s Final Report and the Recommendations contained within it.
The U.S. should create a new non-governmental Innovation Accelerator modeled after the successful In-Q-Tel program to invest in small and mid-cap companies creating technologies that address critical needs of the United States.