The federal government, like the private sector, is turning to artificial intelligence to improve operations and reduce busywork plaguing its sometimes overworked staff.
And like most businesses, federal agencies face obstacles along the way, including a lack of software tools and data infrastructure required for cutting-edge data crunching.
At an A.I. conference last week at Stanford University, Stanford law professor David Engstrom discussed the challenges facing the federal government’s foray into machine learning. In many ways, the challenges mirror those facing corporations, like having employees how know how to operate sophisticated machine-learning software.
Engstrom shared some preliminary findings from the Stanford Policy Lab, which has analyzed technology use by the federal government. The team will eventually present the analysis to the Administrative Conference of the United States, a federal agency intended to improve government processes, to create guidelines for agencies using machine learning.
For the project, Engstrom said members of the policy lab analyzed about 150 Federal departments and agencies to find how they are using machine learning. Ultimately, the researchers identified 171 different uses.
Two of the leading agencies were the Securities and Exchange Commission and the Social Security Administration, he said.
The SEC became savvy in machine learning because it had to keep up with the rapidly changing financial sector. Engstrom described the commission as having a “strong innovation culture” that is “way ahead of most other agencies in terms of the development of these tools.”
The SSA developed its machine-learning chops due to the “entrepreneurial efforts of a few employees” and a recently retired judge, Gerald Ray, who helped spearhead data-crunching projects, Engstrom said. The judge hired lawyers who had computer-programming skills and then let them work on data-crunching projects.
Businesses can learn from these two agencies and their machine-learning successes. For instance, the SEC’s tech-focused culture requires its technical and administrative staff to routinely “gather and compare notes,” ensuring that everyone agrees on which machine learning projects to pursue, Engstrom said.Read More