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Integrating AI Into Arbitration: Balancing Efficiency With Fairness and Legal Compliance

Tariq K. Alhasan

Conflict Resolution Quarterly2025https://doi.org/10.1002/crq.21470article
ABDC B
Weight
0.71

Abstract

The integration of artificial intelligence (AI) into arbitration marks a significant transformation in alternative dispute resolution, aiming to enhance efficiency, objectivity, and accessibility. Advanced AI systems now extend beyond administrative tasks to analyze complex legal data, predict case outcomes, and even generate arbitral awards. This evolution addresses the growing volume and complexity of international disputes, particularly in commercial and investment arbitration. However, the adoption of AI introduces profound legal and ethical challenges. Key concerns include the absence of human judgment, potential biases embedded in AI algorithms, and the opacity of their decision‐making processes, accountability issues, and data privacy risks. Critically, current legal frameworks such as the New York Convention were not designed to accommodate AI‐generated awards, raising questions about their legitimacy, procedural fairness, and enforceability. This article explores these intersections, focusing on how AI impacts arbitration's efficiency and objectivity, the legal and ethical challenges arising from AI integration, and the extent to which existing legal frameworks accommodate AI‐generated awards. Employing a multidisciplinary approach that includes legal scholarship, case studies, and technological research, the analysis examines the practical implications of AI in arbitration and the specific enforcement challenges of AI‐generated awards. The article concludes with recommendations for regulatory reforms and the adoption of hybrid AI‐human models to balance technological benefits with the necessity for human oversight and ethical accountability.

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https://doi.org/https://doi.org/10.1002/crq.21470

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@article{tariq2025,
  title        = {{Integrating AI Into Arbitration: Balancing Efficiency With Fairness and Legal Compliance}},
  author       = {Tariq K. Alhasan},
  journal      = {Conflict Resolution Quarterly},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/crq.21470},
}

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Integrating AI Into Arbitration: Balancing Efficiency With Fairness and Legal Compliance

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Evidence weight

0.71

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.83 × 0.4 = 0.33
M · momentum1.00 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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