Today I am quoted in a story in Wired about Legal Tech and its role in shaping change within the legal industry …
Starting with the original Milgram research in the 1960’s to the Dodds, Muhamad & Watts (2003) paper in Science, the exploration of the social distance between individuals in society has been a topic of interest to many scientists. This new release from researchers at Facebook highlights that social distance is indeed declining. For those who might be interested – I detail in these slides and these slides the history of the small world research.
It is a global battle for talent and labor market(s) are full of noisy signaling (such as elite credentialing, etc.) The question for many organizations is how to arbitrage the existing labor market(s) and find undervalued talent. Competitions such as Kaggle, etc. allow for serious and verifiable demonstrations of skill which can help overcome the strong prestige laden priors held by many managers (or in this case VC’s).
HT: RC Richards
The example above is an algorithmic system that enhanced by the use of crowd based teaching. It is a useful example of the creativity employed by those in the machine learning research community. It is also instructive (at broader level) of the cutting edge approaches used in all of predictive analytics / machine learning.
In discussing legal prediction or the application of predictive analytics in law, we often try to start by highlighting The Three Forms of (Legal) Prediction: Experts, Crowds and Algorithms. These are really the only streams of intelligence that one can use to forecast anything. Historically, in the law – experts centered forecasting has almost exclusively dominated the industry. In virtually every field of human endeavor, there have been improvements (sometimes small to sometimes large) in forecasting which have been driven in the move from experts to ensembles (i.e. mixtures of these respective streams of intelligence – experts, crowds + algorithms).
Through our company LexPredict and in our research, we have been working toward building such ensemble models across a wide range of topics. In addition, we have engaged in a public display of these ideas through Fantasy SCOTUS, our SCOTUS prediction algorithm and through the identification of non-traditional experts (i.e. our superforecasters which — unlike most lawyers — are folks that have actually been benchmarked in their predictive performance). Finally, we have demonstrated the usefulness of SCOTUS prediction in a narrow subset of cases that actually move the securities market.