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 Congress moves forward with the appropriations process, both the House and Senate have proposed various provisions related to artificial intelligence (AI) and machine learning (ML) across different spending bills.
The looming competition for global talent has brought forth a necessity to evaluate and update the policies concerning international visa holders in the United States.
Despite the hype, the uptake of geothermal energy is still relatively low, with only 0.4% of U.S. electricity generated by geothermal.
Responsible governance is crucial to harnessing the immense benefit promised by AI. Here are recommendations for advancing ethical, high-impact AI with thoughtful oversight.