Artificial Intelligence (AI) is becoming a mainstream technology in a wide array of industries.

For example, retail, pharmaceutical and the legal industry continuously seek innovative approaches for improving their day-to-day operations and maximising profit. In the legal sector, this shift towards the adoption of AI is motivated by the need to replace some laborious processes with automation and optimize resources. It is, therefore, not surprising that recent years have seen an increasing reliance on AI for tasks within the field of legal analysis. This typically involves the use of AI for analysing, evaluating, and reviewing legal documents and data bases as well as deriving insights from past litigations and have seen relative success in domestic litigation and/or arbitration. For example, lawyers may rely on AI to inform their assessment of a case and in some circumstances even their chances of success before a given judge or court. However, the predictive potential of such legal analysis is often challenged by the limited availability of data needed to train the AI system.

These challenges are accentuated in the context of international arbitration and disputes where (i) awards are often not published, (ii) a stable body of decision makers or arbitrators is missing, (iii) there is greater variability in terms of possible options. Despite these limitations, the development of AI in international arbitration promises great benefits. For example, recent efforts have led to the development of international arbitration data bases, which may be used not only by the parties to a dispute, but also by litigation funders to inform their assessment of the viability of a case. Additionally, research has shown that court behaviour can be predicted to some degree. Consequently, as third-party litigation funding continues to gain traction around the globe, the predictive potential of AI provides funders with tools to better understand the value and quantify the risk or uncertainty associated with a case. In other words, funders may use AI as part of their risk assessment and funding decision-making.  As highlighted, the confidential nature of international arbitration has so far hindered progress in this area. As a result, the industry’s continuous effort to create arbitral databases such as Dispute Resolution Data (DRD) and others is essential to allow the benefits of AI in international arbitration to be realised in the future.

     Ghislain Landry TSAFACK, PH.D., Head of Data Science at Elemental Concept

Elemental Concept is headquartered in London, England