Big Data Seeks to Predict Likelihood of Bills to Pass in Congress

Big Data

Photo by: Cezary p via Wikimedia Commons


FiscalNote, a company that uses big data and AI to deliver predictive analysis of governmental action and determine its impact, has created a “better way” to know the hidden components that will help a bill pass in the Congress for it to eventually become a law.
Using big data, FiscalNote’s CEO Timothy Hwang and childhood buddy Gerald Yao introduced a software that crawls through various government websites and pulls data from about 1.5 million bills in the Congress. It also gathers data across city councils and state legislatures to know the possibility of each bill to pass in the Congress.

The self-learning technology is also said to be capable of reading through phrases and contextual keywords that will help influence the bill’s success. The Washington-based company that has over 200 clients using their government relationship management platform also combines the phrases and keywords with the details about the bill’s legislator’s voting records and the sponsors. This will allow the technology to spit out the percentage of getting the bill passed, said the company.

The FiscalNote interface reveals a map of the country and shows the areas where bills remain pending. Once a user clicks on a certain bill, it presents a timeline along with its weaknesses and strengths. It also displays the percentage chance of getting it passed.

A client of the company and National Education Association Center for Advocacy’s senior director Mary Kusler claimed that the algorithm is helpful in determining the bills that would create a real impact on teachers and schools.