This kind of analysis looks like a step in the right direction:
According to the team’s analysis, seven states, including Arkansas, Georgia, Idaho, and Kansas, all had similar laws on the books. Similar bills were being considered in states like Maryland and Oregon, and had already died in Florida and Minnesota. In total, very similar bills had been introduced 73 times around the country. The video below shows one of the earliest examples showed up in South Carolina in 2010.
Like a plagiarism detector, the prototype can detect similar language in different bills. Yet unlike in a college class, this isn’t always a bad thing. “We avoided using the word plagiarism,” says Joe Walsh, an assistant professor at the University of Chicago and mentor to the Data Science for Social Good team. “If a bill can save lives, I would want that bill passed all 50 states.”
(Via The Right Coast.)
So what. If a law is a good idea, then other states can and should copy it. If it is a bad idea, its a bad idea even if it is original.
Currently, it is too costly for most voters to obtain the information needed to evaluate legislation, so they don’t try — rational ignorance. The one thing voters can easily see is the name of a bill, and many bad laws with misleading names get passed.
Data mining that revealed the preferences of political insiders would make it easier, perhaps much easier, for interested voters to figure out what is really driving a bill. I don’t see any downside to making this kind of information more readily available.
>> I don’t see any downside to making this kind of information more readily available.
I agree. But transparency is why the politicos will immediately move to make that information inaccessible.
How are they going to do that? Bills are public. Even if they block public access to bills before votes, the resulting laws are public and thus subject to textual analysis that can help to ID who was behind what. Maybe the committee staff will start rewriting the drafts lobbyists submit to them, but that probably won’t happen much, because of the amount of work required.