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The Impact of Third-Party Litigation Finance and Predictive Analytics on International Arbitration

The Impact of Third-Party Litigation Finance and Predictive Analytics on International Arbitration

A developing sector of the economy, third-party litigation finance (TPF) involves speculative investors backing legal claims in return for power. Despite the ICSID Convention and UNCITRAL Arbitration Rules' permission, arbitral tribunals seldom grant security for costs. To curb baseless claims and correct the imbalance between financed investors and the government, security for expenses serves as a systemic safeguard. An order on expenses is not necessary only because a funder is present; instead, the petitioner must prove that an order for security of costs is unnecessary. The outcome of the proceedings determines the third-party funder's payment, and control or influence over the arbitration procedure may result in baseless claims and hinder settlement discussions. The definition of "frivolous" claims is crucial, and it hasn't been given much thought in the context of ISDS yet. The Internet of Things has improved human lives, businesses, and worldwide trade, but machine intelligence now has the means to sustain itself thanks to the automated data gathering, storing, and retrieval processes.

The trendy term artificial intelligence (AI) has sparked worries that robots would eventually surpass people in intelligence. However, as AI is utilized for recommender systems and information retrieval in industries including marketing, healthcare, life sciences, conversational AI, HR tech, and search engines, it has little direct connection to the legal sector. NLP is utilized in the legal industry for recommender systems and information retrieval, automating tasks that were once completed by attorneys. More data-driven research is now possible thanks to artificial intelligence (AI), especially in the field of legal predictive analytics, which uses data extraction to identify patterns and trends in behaviour. A subset of strategic analytics, legal predictive analytics seeks to carry out strategic research and produce insightful policy suggestions for decision-makers. However, because there are many different formatting styles and a wide range of final awards, automating aspects beyond the ISDS subdiscipline is difficult.

A frequent problem in international business arbitration is determining which law will apply. Funders can use predictive analytics to make well-informed judgments about which legislation will apply in a given dispute. The content of Investment Agreements (AAs) is automatically annotated and mapped by the Electronic Database on Investment Treaties (EDIT) using automated content analysis. Although this technology is still in its early stages of development, it should be able to conduct a thorough legal examination and identify the most suitable rule for choice of law analysis. Funders look for patterns in due diligence, individual appointments, and experience using statistical methods and data mining. The science of writing styles, known as stylometry, may be used to determine who wrote a certain piece. The use of this technology in investor-state arbitration will only grow.

The use of predictive analytics in Investment Treaty Settlements (ISDS) has not received enough attention in terms of its legal and economic ramifications. One major area of worry is the paucity of existing literature and conversation around the overcommercialization of ISDS. Critics have pointed out that predictive analytics relies on arbitrary algorithms to find connections and ignores causal inferences and explicit reasoning, which are fundamental to court rulings. Concerns regarding datafication and unconscious biases have arisen as a result of international law's disregard for legal argument and lack of contextual and conceptual frameworks. Predictive analytics can identify arbitrators who make judgments based on reasons other than their stated rationales by uncovering hidden correlations between variables in arbitrations and awards. Funders who have an external interest in profit maximization rather than merely the termination of litigation may also possess this data.

Concerns over third-party funding's (TPF) validity and its effects on the judicial system have been raised by the growing use of TPF in international commercial and treaty arbitration. In an area as complex and ambiguous as law, the use of statistical heuristics can be predictive and instructive; yet, the problem for ISDS is not that parties look too knowledgeable or misinformed by a purported numerical percentage. Anything that is technologically manipulated nearly always becomes more useful, and the introduction of automation has made this trend much more pronounced. Member states are debating and partially agreeing upon regulatory measures as a result of the growing usage of TPF. With the 2022 Amended Arbitration Rules, which modify the TPF framework, ISDS has seen the first institutional reaction to requiring disclosure of TPF agreements. The amendment mandated the parties give written notification of TPF either as soon as the arbitration request is registered or as soon as any post-registration TPF arrangements are completed.

Investment treaty arbitration parties have come to favour third-party financing (TPF), which uses big data and artificial intelligence to assess and forecast litigation risk. Member states, however, are worried that TPF may exacerbate the unbalanced structure already present in the investor-state dispute settlement (ISDS) system. International legislators have presented draft legislation to control the use of TPF to solve these problems. This thesis discusses the role that TPF quantitative modelling plays in driving up prices, creating a perception of bias, and over-commercializing ISDS. There are serious worries regarding TPF's commitment to making impartial and equitable choices as it becomes more commercialized and digitalized. The 2022 Amended Arbitration Rules mandated that parties give written notification of TPF either as soon as the arbitration request is registered or as soon as any post-registration TPF arrangements are completed. The success and future of the ISDS system are in jeopardy.

  • Arbitral tribunals rarely grant security for costs despite TPF's presence.
  • AI aids funders in assessing litigation risks and legal decisions but raises concerns about bias.
  • 2022 Amended Arbitration Rules mandate disclosure of TPF arrangements to address imbalance concerns.

BY : Vaishnavi Rastogi

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