Wednesday, March 12, 2014

Spotlight: Ravel Law - introducing graph analytics to law research

Pranav Singh reached out to me, as he is a data scientist working with Ravel Law for analyzing law related datasets. It seems like an interesting vertical of applying big data analytics to court decisions.

Recently Ravel Law started to incorporate graph data into their analysis. While some of their research is proprietary, they where kindly willing to share some published results. Pranav sent me a paper by Fowler which is named "Network Analysis and the Law".  It shows that using basic pagerank algorithm (hub / authorities) you can get very deep insights into supreme court decision and their importance. The algorithm is rather basic but the applications for the Law vertical are rather new, at least to me.

1 comment:

  1. Such models can be useful but are always modeling a slice of the supreme court. I don't recall any models of the supreme court being tested against "The switch in time that saved the nine"

    There have been other models, Glendon Schubert's "The judicial mind revisited: pyschometric analysis of Supreme Court ideology" comes to mind. Multi-dimensional factor analysis. Interesting in part because Schubert taught himself factor analysis on a rotary calculator.

    There was a spate of court decision making modeling in the 1960's and 70's. I haven't kept up with it so I don't know the current state of the field.