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.
As people become less able to distinguish between what is real and what is fake, it has become easier than ever to be misled by synthetic content, whether by accident or with malicious intent. This makes advancing alternative countermeasures, such as technical solutions, more vital than ever before.
The next administration should establish a Participatory Technology Assessment unit to ensure federal S&T decisions benefit society.
AI is transforming how children learn and live, and policymakers, industry, and educators owe it to the next generation to set in place a responsible policy that embraces this new technology while at the same time ensuring all children’s well-being, privacy, and safety is respected.
A peer support option should be integrated into the 988 Suicide and Crisis Lifeline so that 988 service users can choose to connect with specialists based on a shared lived experience.