Paul L. Caron

Thursday, February 6, 2020

Harvard Law School Legal Tech Startup ArbiLex Uses AI To Settle Arbitrations

Forbes, ArbiLex, A Harvard Law School Legal Tech Startup, Uses AI To Settle Arbitrations:

ArbiLexIsabel Yang, 30, realized the qualitative nature of law represented an opportunity for technology to resolve legal disputes, creating ArbiLex as a result. ArbiLex is a data analytics startup for international arbitrations, leveraging artificial intelligence (AI) to help parties reach resolutions quickly and efficiently. The Cambridge, Massachusetts-based company was incubated out of the Harvard Innovation Lab. ...

ArbiLex’s product is a predictive data analytics tool that leverages Bayesian machine learning (ML) to help international litigators use data to complement their intuition in resolving arbitration cases.

Bayesian ML differs from traditional ML algorithms as probabilities determined in the beginning, intermediate, and final output of a model rely on initial, reasonable guesses of an event happening, called the prior, instead of only building on the observed frequency of an event occurring. A Bayesian approach to machine learning is preferred for this particular problem because one can leverage an expert’s opinion to quantify the prior probability of a given factor in a case. An informed prior is critical to the success of getting a sensible posterior probability, or a probability that coherently adjusts the prior beliefs from limited past data. The confidential nature of international arbitrations reflects how a Bayesian approach has inherent advantages when compared to frequentist probability underpinning many machine learning models. Furthermore, given the need for a prior, which generally comes from an expert, the output from a Bayesian ML model can potentially be explained how the algorithms arrived at a particular conclusion. The key to the success of the startup’s product is access to experts that international litigators and litigation funds need to resolve these disputes in their favor.

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