Artificial intelligence can predict which congressional bills will pass

Matthew Hutson at Science: “The health care bill winding its way through the U.S. Senate is just one of thousands of pieces of legislation Congress will consider this year, most doomed to failure. Indeed, only about 4% of these bills become law. So which ones are worth paying attention to? A new artificial intelligence (AI) algorithm could help. Using just the text of a bill plus about a dozen other variables, it can determine the chance that a bill will become law with great precision.

Other algorithms have predicted whether a bill will survive a congressional committee, or whether the Senate or House of Representatives will vote to approve it—all with varying degrees of success. But John Nay, a computer scientist and co-founder of Skopos Labs, a Nashville-based AI company focused on studying policymaking, wanted to take things one step further. He wanted to predict whether an introduced bill would make it all the way through both chambers—and precisely what its chances were.

Nay started with data on the 103rd Congress (1993–1995) through the 113th Congress (2013–2015), downloaded from a legislation-tracking website call GovTrack. This included the full text of the bills, plus a set of variables, including the number of co-sponsors, the month the bill was introduced, and whether the sponsor was in the majority party of their chamber. Using data on Congresses 103 through 106, he trained machine-learning algorithms—programs that find patterns on their own—to associate bills’ text and contextual variables with their outcomes. He then predicted how each bill would do in the 107th Congress. Then, he trained his algorithms on Congresses 103 through 107 to predict the 108th Congress, and so on.

Nay’s most complex machine-learning algorithm combined several parts. The first part analyzed the language in the bill. It interpreted the meaning of words by how they were embedded in surrounding words. For example, it might see the phrase “obtain a loan for education” and assume “loan” has something to do with “obtain” and “education.” A word’s meaning was then represented as a string of numbers describing its relation to other words. The algorithm combined these numbers to assign each sentence a meaning. Then, it found links between the meanings of sentences and the success of bills that contained them. Three other algorithms found connections between contextual data and bill success. Finally, an umbrella algorithm used the results from those four algorithms to predict what would happen…. his program scored about 65% better than simply guessing that a bill wouldn’t pass, Nay reported last month in PLOS ONE…(More).