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

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Legal disputes between two entities vary in complexity and scope. With criminal law, the outcome of these cases is seen as discrete (guilty, not guilty, acquittal, mistrial). In civil law, a discrete view of the consequences of a legal ruling is inappropriate. The size of a settlement in a civil case varies. With claims resolved through international arbitration, the outcomes are on the order of millions and billions of dollars. Lawyers arguing these cases rely on intuition, precedent, and incomplete information. Isabel 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.

In today’s global economy, business relationships are established and maintained on an international scale. When these relationships breakdown as the parties find themselves in disagreement, international arbitration exists as an option to resolve these disputes. There are many advantages to pursuing international arbitration as a resolution mechanism, such as enforceability, neutrality, confidentiality, and more. However, one of the main difficulties of international arbitration is the length of time a dispute takes to be resolved. Latham & Watkins, a prominent global firm with notable expertise their transactional and regulatory area practices, states, “for a substantial and complex dispute, arbitration typically takes about

12-18 months from commencement to the final hearing.” For the entities with a material stake in this case, the prolonged efforts to reach an outcome have a detrimental effect on all involved (with the exception of the lawyers who bill by the hour). Shortening the length of time needed to resolve international arbitrations is a crucial driver of a potential market in need of a solution.

Estimating the global market arbitration size is difficult due to a lack of data tracking the industry, as the agreements from an arbitration settlement can and usually remain undisclosed. However, understanding the volume and size of these disputes can provide an estimate of the market size. Global Arbitration Review is a leading online publication on law firms that specialize in international arbitration cases. The digital law reporting outlet notes that the “total value” of the pending cases of their top 30 ranked firms is over $2 trillion. Assuming that these firms’ fees are 10% of the total value of the pending cases, the international arbitration market can be roughly estimated to be $200 billion. A product that could help resolve these disputes faster could capture a small portion of the potential market value in fees.

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.

The main benefit of ArbiLex’s product is mapping out risk factors associated with an arbitration case. The startup’s algorithms can help benchmark and quantify probabilities, which allows users to think more probabilistically when assessing settlement outcomes as opposed to maintaining a binary mindset. The deep, technical nature of Bayesian ML and the arcane knowledge of the law is necessary for a team to tackle this problem successfully. 

Yang, a graduate of Oxford University with a Master’s degree in Economics and a Juris Doctor from Harvard Law School, is well versed in the legalese and economics of international arbitrations. Her economic experience comes from her time at the World Trade Organization and the National Treasury of South Africa. Her understanding of international arbitrations stems from her stints as a summer associate at the law firms Freshfields Bruckhaus Deringer and Shearman & Sterling. AribLex’s AI Lead, Raj Agrawal, is an MIT Ph.D. candidate in Computer Science, specializing in machine learning and predictive inference. The two’s formidable partnership and cross-functional expertise could make ArbiLex an invaluable tool in international arbitrations.

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