Mayer-Schönberger and Cukier on Big Data and Law

Professor Dr. Viktor Mayer-Schönberger of the Oxford Internet Institute and Kenneth Neil Cukier of The Economist gave a presentation entitled Big Data — and Its Dark Side, 6 March 2013, at the Berkman Center for Internet and Society at Harvard University.

The presentation concerned their new book entitled Big Data: A Revolution That Will Transform How We Live, Work, and Think (Houghton Mifflin, 2013).

The presentation includes some examples concerning legal data, including an analysis of topics discussed in proceedings of the British House of Commons, a study of the association between the ideology and citation practices of U.S. Supreme Court Justices, and predictive policing.

Tauberer: New GovTrack Bill Prognosis Methodology Page, with Charts

Dr. Joshua Tauberer has created a new bill prognosis methodology page for GovTrack, his U.S. federal open legislative data service.

The page includes (on right screen) three tabs of charts demonstrating output and functioning of the prognosis methodology. The third tab shows charts of precision vs. recall results, and is intended expressly “for machine learning researchers.”

HT @JoshData

Tauberer: Changes to GovTrack Bill Prognosis

Dr. Joshua Tauberer of GovTrack has posted Bill prognosis gets a few improvements, at the GovTrack Blog.

He writes:

…I’m adding three new factors to GovTrack’s analysis: whether the bill was introduced in the first 90 days of the Congress, whether it was introduced in the first year, and whether it was introduced in the last 90 days of the Congress. You can now see that last one in the factors for S. 3637, for example.

The post describes his decision to incorporate a component of the model described in: Yano et al.: Textual Predictors of Bill Survival in Congressional Committees.

For more details please see the complete post.

HT @JoshData

Yano et al.: Textual Predictors of Bill Survival in Congressional Committees

Tae Yano and Professor Dr. Noah A. Smith, both of Carnegie Mellon University Language Technologies Institute, and Professor Dr. John D. Wilkerson of the University of Washington Deaprtment of Political Science, presented a paper entitled Textual Predictors of Bill Survival in Congressional Committees, at New Directions in Analyzing Text as Data 2012, a conference held 5-6 October 2012 at the Harvard University Institute for Quantitative Social Science.

Here is the abstract:

A U.S. Congressional bill is a textual artifact that must pass through a series of hurdles to become a law. In this paper, we focus on one of the most precarious and least understood stages in a bill’s life: its consideration, behind closed doors, by a Congressional committee. We construct predictive models of whether a bill will survive committee, starting with a strong, novel baseline that uses features of the bill’s sponsor and the committee it is referred to. We augment the model with information from the contents of bills, comparing different hypotheses about how a committee decides a bill’s fate. These models give significant reductions in prediction error and highlight the importance of bill substance in explanations of policy-making and agenda-setting.

A notable technology related to this research is the new probability-of-bill-passage feature on Dr. Joshua Tauberer’s GovTrack service.

Click here for Dr. Tauberer’s comment on the Yano et al. paper.

An interesting discussion among academics and developers arose on Twitter in response to a tweet about this paper.

GovTrack Adds Probabilities to Bill Prognosis

Dr. Joshua Tauberer of GovTrack has posted Even Better Bill Prognosis: Now with Real Probabilities, on the GovTrack Blog.

In this post, Dr. Tauberer describes the new probability-of-passage figure added to GovTrack’s bill prognosis feature. According to the post:

For the data wonks out there, the new prognosis is based on a logistic regression model. The model predicts a bill’s success based on the following binary factors:

• the title of the bill (such as if it is a bill to name a post office)
• whether the sponsor is a member of the majority party (in the House or Senate as appropriate)
• whether the sponsor is the chair, ranking member, or a member (if majority party) of a committee that the bill has been referred to
• if any cosponsor is the chair or ranking member (most senior minority party member) of a committee the bill has been referred to
• if there are 3-5 cosponsors of the bill serving on a committee the bill has been referred to
• if the bill has a cosponsor from both parties
• if the bill’s sponsor is in the majority party and at least 1/3rd of the cosponsors are from the minority party

Success is for bills if they are enacted and for resolutions if they successfully reach the end of their life cycle (simple resolutions passed, concurrent resolutions passed by both chambers, joint resolutions enacted). [...]

Dr. Tauberer has also posted the following request:

Anyone have ideas for more factors to consider for predicting which bills will pass?

For more information, please see Dr. Tauberer’s post, or contact him on Twitter at @JoshData.

HT @waldojaquith.