Technology-Based Disputes: Striking a Harmony Between Innovation and Reliability
The world of arbitration is on the brink of a significant transformation, with artificial intelligence (AI) poised to introduce capabilities that could redefine the very nature of dispute resolution. Over the next five years, AI is expected to fundamentally alter arbitration proceedings, ushering in a new era of efficiency and effectiveness.
International institutions have recognised the potential of AI in arbitration and have taken steps to promote uniform standards. Major arbitral bodies such as the International Chamber of Commerce (ICC), the Stockholm Chamber of Commerce (SCC), the Deutsche Institution für Schiedsgerichtsbarkeit (DIS), the Vienna International Arbitral Centre (VIAC), the London Court of International Arbitration (LCIA), the Swiss Chambers’ Arbitration Institution (SCAI), and the Singapore International Arbitration Centre (SIAC) have published guidelines on the use of AI in arbitration.
Effective mitigation of risks associated with AI requires a combination of technical and procedural safeguards. These include strong encryption protocols, proprietary AI tools, confidentiality undertakings, a tiered approach to AI usage based on data sensitivity, and regular security audits and awareness training. Arbitral institutions are encouraged to create guidelines, model clauses, and best practice recommendations for AI usage while maintaining human oversight and transparency.
While AI offers numerous benefits, it is not without its challenges. Cooke warns against complacency, stating that AI might not be able to replace the analytical reasoning and problem-solving of human arbitrators. The disconnect between algorithmic reasoning and evolving legal standards in AI use creates vulnerability, particularly in jurisdictions with broadly interpreted public policy exceptions to enforcement.
AI could assist in the monitoring and enforcement of awards, tracking compliance with the terms of the awards, delays, breaches, and enhancing the overall effectiveness of the arbitration process. It could also be used to analyze vast datasets of past arbitration cases to provide parties with more accurate predictions regarding potential outcomes, settlement ranges, and the likely success of specific arguments.
The procedural landscape of arbitration may undergo dramatic transformation with automated case management systems, real-time language translation, and transcription. However, the integration of AI systems introduces multi-layered confidentiality risks, making confidentiality a cornerstone of the process. Lawyers need to have a clear understanding of how an AI platform may use confidential information inputted by the user.
The absence of uniform regulations necessitates innovative approaches to standardization that preserve arbitration's inherent flexibility. Bhat advocates for a multi-faceted strategy emphasizing soft law mechanisms as the most promising path forward. This includes principle-based guidelines, certification programs for AI vendors, clear disclosure requirements, and transparent collaboration among stakeholders.
AI gives rise to the prospect of the fundamental democratisation of access to arbitration through predictive analysis and risk assessment. However, it might take more than five years for fully AI-driven arbitration to emerge, and parties may tolerate a risk that AI might make a mistake in deciding a dispute.
In conclusion, the integration of AI into arbitration promises a future of increased efficiency, accuracy, and accessibility. However, it is crucial to approach this transformation with caution, ensuring that the inherent flexibility and confidentiality of arbitration are preserved, and that the risks associated with AI are mitigated.
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