It is my great pleasure to visit with Primerus and its associated law firms and deliver an address at its 2018 Annual PDI Convocation.
CLOC Panel – Legal AI in Real Life — (Cisco, Liberty Mutual, Spotify) (I was filling in for Julian T. from Google) — I discussed the contract analytics project we are undertaking with Cisco / Elevate and other applied A.I. / Analytics Projects that the LexPredict Team is undertaking with corporate legal departments !
The March of Machine Learning as a Service #MLaaS rolls on !
Regarding the quote above — we agree. However, it should be noted that the ‘simple substitution story’ works at the aggregate level over a period of time with the simple assumption that the tasks which comprise current jobs can be decomposed and recombined into new jobs. Certainly, institutions (both firms and public sector) will take some period of time to be able to repackage certain existing jobs. Thus, lags are to be expected. < Click Here to Access the Article >
ABSTRACT: In this paper, we investigate the application of text classication methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the inuence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classiers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling
As was noted this was not good enough for NIPS 2017 (#Fail) … (but see here)
This is an interesting development – click here to access story!
From Science News – “In the new study, Weng and his colleagues compared use of the ACC/AHA guidelines with four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks.”
The underlying paper was published in Plos One (one of my favorite journals) and the location where we recently published our US Supreme Court Prediction paper. In that paper, we use a time evolving random forest (with the novel twist of a tree burning protocol).