The Rise of AI in International Arbitration: Opportunities and Legal Challenges

1. ABSTRACT

This report critically examines the evolving part of Artificial Intelligence (AI) within transnational arbitration, a field decreasingly embracing technological advancements for enhanced effectiveness and cost- effectiveness. It explores current AI operations, similar as document analysis, textbook drafting, legal exploration, and case operation, pressing their transformative eventuality in streamlining disagreement resolution processes. The report scrutinizes the complex ethical and legal challenges posed by AI integration, including issues of translucency (” black box” problem), algorithmic bias, confidentiality, the absence of mortal discretion, and enterprises regarding legal query and enforceability of AI- supported awards. It further analyzes the arising global nonsupervisory and ethical fabrics, furnishing a relative overview of institutional responses from bodies like the Silicon Valley Arbitration & Mediation Center (SVAMC), the Chartered Institute of judges (CIArb), and the Stockholm Chamber of Commerce (SCC). The case study of LaPaglia v. Valve Corp. serves as a practical illustration of these challenges, particularly concerning thenon-delegation of adjudicative places and the need for translucency. Eventually, the report delves into the unique environment of AI relinquishment in Indian arbitration, noting both its significant eventuality for addressing inefficiencies and the prevailing nonsupervisory gaps, including the visionary station of judicial bodies and policy considerations by realities like NITI Aayog. The overarching end is to balance technological invention with the enduring principles of mortal judgment, equity, and justice in transnational arbitration.

Keywords:

2. INTRODUCTION

The adaption and objectification of Artificial Intelligence (AI) tools across colorful professional and on-professional spheres has come a common miracle, largely driven by their stoner-friendly and readily available interfaces, which significantly boost their request demand. The legal fraternity, including the transnational arbitration assiduity, is not vulnerable to this technological swell. AI’s integration into transnational arbitration is unnaturally transubstantiating traditional disagreement agreement practices, promising enhanced effectiveness, lesser cost- effectiveness, and bettered access to sophisticated legal analysis. Leading arbitral institutions, similar as the Chartered Institute of judges (CIArb), the Silicon Valley Arbitration & Mediation Center (SVAMC), and the Stockholm Chamber of Commerce (SCC), are formerly admitting and laboriously addressing the profound impact of AI, incorporating ethical and procedural safeguards into their functional guidelines and practices. This report aims to critically examine the evolving part of AI within transnational arbitration. It’ll claw into the current operations of AI, exploring the myriad openings it presents for streamlining processes and enhancing issues. coincidently, the complex ethical and legal challenges that arise from AI’s integration, including issues of translucency, bias, confidentiality, and the abecedarian nature of mortal judgment in adjudication. The report will further dissect the arising nonsupervisory and ethical fabrics designed to govern AI’s use in this sphere, furnishing a relative overview of institutional responses. A specific case study, LaPaglia v. Valve Corp., will be explored to illustrate the practical counteraccusations and serve as a real- world test for the principles governing AI integration. Eventually, the report will consider the unique environment of AI relinquishment in Indian arbitration, pressing both its implicit and the prevailing nonsupervisory gaps. The rapid-fire and wide relinquishment of AI in the legal sector, particularly in arbitration, appears to be driven by practical mileage and immediate demand. The emphasis on” stoner friendly” and” readily available” interfaces suggests a bottom- up integration, where interpreters and institutions are embracing AI for its palpable benefits like effectiveness and cost reduction. This still has outpaced the development of formal governance. The presence of ” limited” backing and the” absence of steady international morals” indicates that nonsupervisory bodies are largely replying to being AI use rather than proactively shaping its preface. This dynamic creates a notable pressure between technological invention and the necessary nonsupervisory oversight, where the ultimate is frequently playing catch- up to alleviate pitfalls that have formerly begun to manifest. 

3. THE EVOLVING LANDSCAPE OF AI IN INTERNATIONAL ARBITRATION

Artificial intelligence is highly defined as the capability of a device to perform functions generally associated with mortal intelligence, similar as logic, literacy, and tone- enhancement. This term was first chased by computer scientist John McCarthy in 1956. In recent times, ultramodern AI operations, particularly machine literacy and natural language processing (NLP), have gained significant traction within the legal sector, unnaturally altering how legal professionals approach their work.

Current Application tools: 

AI’s utility in international arbitration spans a wide array of functions, offering transformative capabilities.

• Document Analysis and Review: Generative AI tools, erected on large language models, retain the capability to fleetly search vast databases, excerpt material perceptivity, draw comparisons, and epitomize complex information. This capability markedly reduces the need for laborious homemade labor in document analysis, encompassing tasks similar as searching for and indexing data in testaments, relating disagreement in reiterations, or creating annexures and timelines from substantial document sets. Beyond introductory summarization, AI tools can efficiently review and classify immense amounts of documents and data, simplifying the identification of applicable substantiation and accordingly reducing the time and cost associated with discovery processes.
• Text Drafting and Customization: Tools like ChatGPT, Claude, and LinerAI facilitate the rapid crafting of professionally customized drafts. This includes the ability to eliminate repetitive arguments, generate bespoke drafts for case management tailored to specific case details, and even draft initial arbitral awards based on the merits of a case.   
• Legal Research and Predictive Analytics: AI- powered legal exploration platforms, including Casetext, Westlaw, LexisNexis, and Google, significantly streamline legal exploration and prophetic analytics. AI algorithms can dissect literal arbitration cases to read likely issues or agreements grounded on colorful factors, thereby empowering parties to make further informed opinions regarding pursuing arbitration or concession. Some advanced AI platforms can indeed suggest overlooked case law or update legal authorities in real- time.
• Case Management and Internal Processes: AI backing is decreasingly being stationed for case operation, disagreement resolution services, and internal executive processes within arbitral institutions. Notable exemplifications include the development of an” automated scheduling order tool” designed to induce quick primary calendars for fast- track cases grounded on original hail reiterations. likewise, institutions are exercising tools like ChatGPT and DeepL for refining reiterations related to internal dispatches similar as emails, speeches, and donations. The launch of platforms like the SIAC Gateway, a digital platform easing online case form and real- time access to ongoing SIAC proceedings, exemplifies the modernization of arbitration practices through AI.
• Virtual Hearings and Site Visualization: The London Court of International Arbitration (LCIA) has introduced “LCIA-Digital,” an AI-driven platform that supports virtual hearings and document management. Beyond virtual proceedings, Maxwell Chambers has demonstrated the application of drones and site visualization technology to assist parties and tribunals in arbitration proceedings, offering new perspectives for evidence presentation and analysis.   
• Compliance and Due Diligence: AI can play a pivotal part in assessing the compliance of arbitration proceedings with applicable laws and regulations. It can also prop in due industriousness processes by relating implicit conflicts of interest, enhancing the integrity of the arbitral process.   
• Decision Support: While AI is not generally employed to render final arbitration opinions, it can give inestimable decision support by assaying substantiation, relating patterns, and presenting applicable information to judges, thereby aiding in their decision- making process.

Benefits and Opportunities

The integration of AI into international arbitration offers several compelling benefits:

• Enhanced Efficiency and Speed: AI significantly reduces the reliance on laborious homemade labor, thereby expediting proceedings and accelerating the resolution of conflicts.
• Improved Cost-Effectiveness: By minimizing paperwork, streamlining document operation, and optimizing the association of substantiation, AI contributes to making the process of legal consultancy and arbitration more economically feasible.
• Claimed Accuracy and Reduced Bias: With its capacity to examine large datasets, AI is occasionally posited as being more accurate and less prone to bias than human decision-making.   
• Greater Access to Justice: These technological advancements promises to enhance the overall effectiveness of disagreement resolution, reduce costs, and promote procedural fairness, which is particularly salutary for parties with limited fiscal coffers.   

The harmonious emphasis across all listed operations is on AI’s capacity to enhance effectiveness. Expressions similar as” significantly reduced the input demand of laborious homemade labor,”” aid in textbook drafting,” and” enhance effectiveness, reduce costs” emphasize AI’s mileage in streamlining processes and accelerating mortal capabilities. While AI can perform tasks like drafting and summarization, the language constantly frames its part as” backing” and” support” rather than a full relief for mortal places, especially in core adjudicative functions. This highlights the current perception of AI as an addition tool, designed to support and ameliorate mortal performance rather than to completely automate complex legal judgment.

A notable pressure arises when considering AI’s pledge of equity. While AI is” said to be more accurate and less prejudiced than mortal decision- timber,” the same source incontinently introduces caveats, noting that AI is” devoid of emotional intelligence and may miss the fine details”. likewise, it’s conceded that” AI systems are only as unprejudiced as the data they’re trained on”. This presents a direct contradiction the theoretical pledge of AI’s neutrality versus the practical reality of its essential impulses, which are deduced from its training data, and its incapability to grasp mortal nuance or emotional environment. This incongruity necessitates careful consideration and mitigation strategies in the challenges section.

Tool Name	Primary Applications
Casetext	Legal research
ChatGPT	Drafting and customization, Refined transcripts for internal processes (emails, speeches, presentations)
Claude / Claude.ai	Practical applications, Brainstorming legal positions
LinerAI	Rapid crafting of professionally customized drafts
Everlaw, Lex Machine, LawGeex, Due Diligence	General legal industry applications
TERES	AI transcription tool
Jus AI	AI-powered summarization tool
DeepL	Refined transcripts for internal processes
Drones and Site Visualization Technology	Assisting parties and tribunals in arbitration proceedings (e.g., site inspection)
SIAC Gateway	Online case filing, Real-time access to ongoing SIAC proceedings
Westlaw, LexisNexis, Google	Streamlining legal research and predictive work
LCIA-Digital	Virtual hearings, Document management
SUPACE	Supreme Court Portal for Assistance in Courts Efficiency
SUVAS	Supreme Court Vidhik Anuvaada Software (legal document translation)

Table 1: Key AI Tools and Their Applications in Arbitration

4. Ethical and Legal Challenges of AI Integration

Despite the promising openings, the integration of AI into transnational arbitration introduces a complex array of ethical and legal challenges that want careful consideration.

Lack of Transparency (“Black Box” Issue)

AI systems operate through intricate algorithms, frequently appertained to as” black boxes,” where the precise logic behind their labors or opinions remains obscured, indeed to their inventors. This essential nebulosity directly conflicts with a foundational demand of arbitration the allocation of reasoned awards. The UNCITRAL Model Law on International Commercial Arbitration (Article 31 (2)) explicitly authorizations that an arbitral award must state the reasons upon which it’s predicated, a principle pivotal for icing procedural fairness and enabling judicial review. AI’s opaque decision- making process undermines this principle, elevating the threat that awards may be challenged and potentially set away for lack of translucency, as permitted under Composition V (1)(b) of the New York Convention. Without clear and scrutable sense, parties cannot singly corroborate whether justice has been served, which inescapably erodes trust in the arbitral process itself.   

Algorithmic Bias and Fairness enterprises 

AI systems learn by recycling vast quantities of literal data, which may unfortunately carry” bedded impulses reflecting social prejudices or discriminational practices”. still, reliance on AI in arbitration pitfalls immortalizing” systemic shafts” or impulses, directly contradicting the core arbitration principle of equity, if not precisely regulated and eased. The Supreme Court in BG Group plc v. Argentina underlined the abecedarian necessity of adjudicator equity and fairness, a standard that AI cannot innately guarantee when trained on prejudiced data. This raises profound enterprises about the principle of equivalency before the law and the protection of abecedarian rights within the arbitration frame.

Confidentiality and Data Security Counteraccusations 

Confidentiality stands as a foundation of arbitration, distinguishing it from public judicial proceedings. The saintship of this principle was specially affirmed in cases similar as Fiona Trust & Holding Corporation v. Privalov. still, effective AI training and operation frequently bear access to and processing of large datasets, which may not be readily available or admissible due to strict confidentiality rules essential in arbitral proceedings. Planting AI without acceptable safeguards thus risks violating this pivotal confidentiality, potentially exposing sensitive marketable information and violating parties’ licit prospects of sequestration. Indeed,” limited” AI backing has formerly been linked to concerning incidents of” data thefts” and” leaks of nonpublic information”.

Absence of mortal Discretion and Empathy 

AI, by its veritably nature, is” devoid of emotional intelligence” and lacks the” moral judgment, and strictness rates necessary for just disagreement resolution”. This limitation means that AI may yield a” strict interpretation of’ correct’ legal issues, not inescapably what’s stylish for the parties involved”. counting solely on rigid AI algorithms pitfalls leading to” mechanically applied opinions” that fail to address the” substantial equities of difficulties,” thereby weakening the overall legality and acceptance of the arbitration’s outgrowth. Arbitration frequently requires a nuanced understanding of the parties’ circumstances and indifferent considerations, especially in cases decided ex aequo et bono (in equity and good heart), which AI presently cannot replicate.

Threat of Judicialization and Procedural severity 

AI’s reliance on precedent and literal judicial data carries the implicit to” judicialize” arbitration, assessing rigid procedural morals that contradict arbitration’s innately flexible and party- driven nature. Courts constantly emphasize procedural autonomy in arbitration to promote effectiveness and party autonomy, principles honored in the UNCITRAL Model Law and institutional rules like the ICC Arbitration Rules. An AI- driven adherence to court- suchlike formalism, thus, pitfalls undermining these foundational principles, potentially eroding one of arbitration’s crucial advantages over traditional action.

Legal query and Enforceability of AI- supported Awards

 A significant concern is that current arbitration laws and transnational conventions do not explicitly fete AI as a valid adjudicator, which creates” profound query about the legality and enforceability of AI- generated awards”. The New York Convention, 1958, for case, requires awards to misbehave with abecedarian due process morals, including equity and the right to be heard. AI’s opaque processes and implicit impulses cast serious dubieties on compliance with these norms, mainly adding the threat of awards being refused recognition or enforcement by courts worldwide.

Responsibility

In conventional arbitration, the adjudicator bears direct responsibility for their judgment. still, with the adding involvement of AI, a critical question emerges” who will be in- charge of taking opinions in an AI- grounded arbitration?”. The prolixity of responsibility between mortal judges and AI systems creates a complex challenge for responsibility, particularly when crimes or impulses lead to unjust issues. The core challenge of AI integration is n’t simply about implicit abuse, but about the essential pressure between AI’s algorithmic, data- driven nature and the foundational principles of transnational arbitration. 

V. Global Regulatory Frameworks and Institutional Responses

Recognizing the opportunities and challenges posed by AI, colorful institutions and legal bodies worldwide have begun to develop guidelines and fabrics to govern its use in arbitration. These sweats represent a critical step towards icing responsible AI integration.

Emerging Guidelines for AI in Arbitration

Several key guidelines have emerged to address the responsible use of AI by arbitrators and parties:

• Silicon Valley Arbitration and Mediation Center (SVAMC) Guidelines on the Use of Artificial Intelligence in Arbitration: These guidelines, initially prescribed as a draft on August 31, 2023, were formally issued on April 30, 2024. They introduce a “principle-based framework” designed to assist participants in navigating AI’s potential applications.
  • Guideline 2 (Securing Confidentiality): This guideline emphasizes that AI tools should only be used with nonpublic data if they offer robust protection, warning that many generative AI platforms, such as ChatGPT, may store and potentially utilize user input, posing a significant confidentiality threat. Users are advised to thoroughly vet AI tools, redact or anonymize sensitive data, and prioritize privacy compliant platforms to prevent data leakage.   
  • Guideline 3 (Disclosure): While not mandating a general obligation for disclosure of AI tool use, this guideline suggests that disclosure may be warranted in specific circumstances, particularly where due process, honor, or fairness could be impacted. Suggested disclosures include the AI tool’s name and version, how it was used, and the prompt-output record.   
  • Guideline 4 (Duty of Capability and Industriousness): This guideline holds parties and their representatives responsible for verifying the accuracy and reliability of AI-generated outputs, specifically guarding against AI “hallucinations” presumptive but factually incorrect content. It underscores the critical importance of human oversight and professional responsibility in legal drafting.   
  • Guideline 6 (Non-Delegation of Decision-Making Responsibilities): Arbitrators are explicitly prohibited from delegating any part of their personal mandate, especially their decision-making process, to any AI tool. The guideline asserts that AI tools must not replace an arbitrator’s independent analysis of the facts, the law, and the evidence.   
  • Guideline 7 (Respect for Due Process): This guideline stipulates that an arbitrator shall not rely on AI-generated information that is outside the record without making appropriate disclosures to the parties beforehand and, where practical, allowing the parties to comment on it. Furthermore, if an AI tool cannot cite independently verifiable sources, an arbitrator should not assume such sources exist or are accurately characterized by the AI tool.   
• Chartered Institute of Arbitrators (Ciarb) Guideline on the Use of AI in Arbitration: Published in 2025, this guideline is characterized as a non-mandatory “soft law” provision that parties may choose to incorporate. It aims to provide guidance on leveraging the benefits of AI while mitigating risks to process integrity, procedural rights, and award enforceability.
  • Article 8 (Discretion over use of AI by arbitrators): This article permits arbitrators to consider using AI tools to enhance efficiency and decision-making quality. However, it strongly advises that they “should not relinquish their decision-making powers to AI” and “should avoid delegating any tasks to AI Tools… if such use could influence procedural or substantive decisions.” Arbitrators are required to independently verify AI-generated information and maintain a critical perspective, and ultimately, “shall assume responsibility for all aspects of an award, regardless of any use of AI to assist with the decision-making process”.   
  • Article 9 (Transparency over use of AI by arbitrators): This article encourages arbitrators to consult with the parties, as well as other arbitrators on the same tribunal, regarding whether AI tools may be used throughout the arbitral proceedings.   
• JAMS Artificial Intelligence Dispute Rules: Introduced on April 15, 2024, by JAMS, a prominent Alternative Dispute Resolution (ADR) service provider, these rules specifically govern binding arbitrations of disputes or claims administered by JAMS where parties agree to their use, or where the disputes are AI-related. They address the critical issue of maintaining data confidentiality in arbitration proceedings and propose routine inspections for compliance. The rules encompass AI hardware, software, models, and training data. They also limit access to data and materials, making them available only to one or more experts mutually agreed upon by the parties, arbitrator, or tribunal, extending to an “attorney’s eyes only” setup.
  • Rule 16.1. Procedures (b): This rule specifies that the production and inspection of AI systems or related materials are limited to the disclosing party making them available to one or more experts in a secured environment, with experts prohibited from transmitting or removing produced materials. If jointly requested, the Arbitrator may designate experts, preferably from a JAMS-maintained list, with costs generally borne equally by parties, though the Arbitrator retains discretion to shift fees.   
• Court of King’s Bench in Manitoba, Canada Guidelines: These guidelines instruct that court submissions prepared with AI assistance must clearly reflect the manner in which such assistance was sought. They were issued in recognition of the rapidly evolving AI landscape and the inherent difficulty in pinpointing an exact set of regulations for its responsible use.   
• Stockholm Chamber of Commerce (SCC) Guide to the Use of Artificial Intelligence in Cases Administered Under the SCC Rules (AI Companion): Released on October 16, 2024, the SCC AI Companion offers non-binding, flexible guidance aimed at promoting the responsible use of AI while preserving the fundamental principles of fairness, confidentiality, and integrity. It defines an AI system based on the EU Artificial Intelligence Act (Regulation 2024/1689). The guide outlines crucial principles, including Confidentiality (users must be vigilant about how AI tools store user data), Quality Control (AI-generated outputs may perpetuate biases or fabricate evidence, necessitating human oversight and verification), Integrity and Transparency (encouraging disclosure of AI use in core functions), and Non-Delegation of Decision-Making (reiterating that AI tools must not substitute the arbitrators’ personal judgment or legal logic, with ultimate responsibility resting with the arbitral bench).   

Institutional Initiatives and Surveys

Beyond formal guidelines, several arbitral institutions are actively exploring and integrating AI into their operations:

• ICCA Hongkong ’24 Congress Survey Results: A survey conducted at the ICCA Hongkong ’24 Congress sought to ascertain the extent to which arbitral institutions were already utilizing AI assistance. Out of 11 regional and international institutions that responded, 4 confirmed their use of AI in some form. The questionnaire specifically probed AI implementation in Internal Processes, Case Management, Enhancement of current dispute resolution services, and New Products and services incorporating AI.
  • Detailed Examples from Responding Institutions: One institution employed a team of coders and engineers for case management, dispute resolution services, and internal processes, using AI for drafting arbitral awards, documents for parties and arbitrators, and case briefings. A second institution actively promotes informed AI application and has formed an AI-specific working group, identifying over sixty potential areas for AI use, including developing an “automated scheduling order tool”. A third institution uses ChatGPT and DeepL for refining transcripts related to internal processes like emails and presentations, with broader AI implementation under scrutiny. Finally, a fourth institution uses AI for internal material production and has notably issued guidelines for AI-related disputes. All 11 institutions acknowledged AI’s potential and are considering integration, collectively agreeing on its capacity to improve work efficiency. The remaining 7 institutions not yet using AI expressed interest in deploying it for case management and internal processes.   
• SIAC Symposium 2024 Insights: The afternoon session of the SIAC Symposium 2024 extensively explored the transformative impact of technology in arbitration.
  • Plenary Panel III: Technology in Arbitration – Knowledge to Implementation to Integration: This session included a live demonstration of TERES, an AI transcription tool. The panel discussed AI’s potential for brainstorming legal positions, demonstrated via Claude.ai’s chatbot function, and summarizing case documents using Jus AI’s summarization tool. While optimism was expressed for practical applications, vigilance against AI “hallucinations” (inaccurate or fabricated information) and the imperative for human oversight were strongly emphasized. The session also showcased the use of drones and site visualization technology by Maxwell Chambers.   
  • SIAC Gateway Launch Reception: The symposium concluded with the launch of the SIAC Gateway, a digital platform facilitating online case filing and real-time access to ongoing SIAC proceedings, further modernizing arbitration practices.   
• HKIAC’s Commitment to Innovation: The Hong Kong International Arbitration Centre (HKIAC) launched “the Hub” on May 9, 2025, an initiative designed to connect arbitrators and legal technology providers through hands-on demonstrations, workshops, and training sessions. HKIAC has a history of pioneering technology integration, including in its 2018 rules, enhanced information security measures in the 2024 rules, expanded virtual hearing capabilities in 2020, and the launch of HKIAC Case Connect for online case management. In 2025, HKIAC also announced free access to Case Digest and a new partnership with Jus Mundi, facilitating the use of Jus AI capabilities for case abstracts.   
• ICC: While the ICC Arbitration Rules do not explicitly address AI, the ICC Commission on Arbitration and ADR’s updated Report on Information Technology in International Arbitration (2022) indicates that 93% of interviewees believe IT has revolutionized arbitrations by streamlining processes and reducing costs. The ICC rules emphasize the impartiality and independence of arbitrators and the requirement for reasoned awards.   

Comparative Analysis of Approaches and Common Themes

A comparative analysis of the emerging guidelines reveals both common themes and unique approaches among institutions:

• Common Themes Across Guidelines (SVAMC, Ciarb, SCC): There is a strong consensus on several fundamental principles.
  • Non-Delegation of Decision-Making: All three guidelines strongly emphasize that arbitrators must not delegate their core adjudicative role or decision-making process to AI tools. They must retain independent analysis and ultimate responsibility for the award.   
  • Accuracy and Verification / Human Oversight: These guidelines consistently highlight the arbitrator’s duty to independently verify the accuracy of AI-generated information, cautioning against “hallucinations” and stressing the need for a critical human perspective to prevent undue influence.   
  • Transparency and Disclosure: All advocate for appropriate disclosures to parties regarding the use of AI, particularly when it impacts due process or core adjudicative functions, and some encourage seeking prior approval.   
  • Confidentiality and Data Security: Given AI’s reliance on large datasets, these guidelines underscore the critical need for robust protocols to protect sensitive and confidential information, warning against data leakage from generative AI platforms.   

• Unique Aspects and Nuances of Institutional Approaches:
  • JAMS Artificial Intelligence Dispute Rules: These rules are distinct in their specific focus on the secure handling and access limitations for AI systems and related materials (Rule 16.1.b), rather than solely on the arbitrator’s adjudicative use of AI. They are specifically designed to govern “AI-related disputes”.   
  • Canadian Court of King’s Bench Guidelines: These guidelines offer a broader legal perspective by addressing AI use in the preparation of court submissions, emphasizing the need for disclosure regarding how AI assistance was sought.   
  • SCC AI Companion: This guide provides a specific definition of AI based on the EU AI Act and includes a “Quality Control” principle that calls for mechanisms to flag AI-generated or altered content.   
  • ICC, LCIA, SIAC, HKIAC: While not having explicit AI guidelines for arbitrators (as of the provided information), these major institutions are actively exploring and integrating technology into their administrative and procedural processes (e.g., e-filing, virtual hearings, online case management platforms, AI transcription/summarization tools). HKIAC’s “Hub” initiative is a unique approach to fostering connections and knowledge exchange between arbitrators and legal tech providers, demonstrating a pragmatic, efficiency-driven approach to AI adoption.   

Despite forming from different institutions and having varying specific focuses, there’s a strong confluence among the SVAMC, Ciarb, and SCC guidelines on the abecedarian principles ofnon-delegation of adjudicative functions, robust mortal oversight, translucency, and the safekeeping of confidentiality. This thickness across different influential bodies suggests that these are widely honored as the most critical enterprises and foundational conditions for maintaining the integrity and legality of arbitration in the age of AI. This confluence is a significant development, indicating an arising global agreement on the minimal ethical and procedural norms needed for responsible AI integration in arbitration, potentially paving the way for unborn harmonized transnational morals.

Table 2: Comparative Analysis of Major AI Guidelines for Arbitrators

Guideline/Rule Name	Issuing Body	Date Issued	Key Principles
Guidelines on the Use of Artificial Intelligence in Arbitration	Silicon Valley Arbitration & Mediation Center (SVAMC)	April 30, 2024	Non-Delegation of Decision-Making (G6), Transparency/Disclosure (G3, G7), Confidentiality/Data Security (G2), Human Oversight/Verification (G4, G7), Principle-based framework.
Guideline on the Use of AI in Arbitration	Chartered Institute of Arbitrators (Ciarb)	2025	Non-Delegation of Decision-Making (A8), Transparency/Disclosure (A9), Human Oversight/Verification (A8), Mitigation of risks to integrity and enforceability.
Artificial Intelligence Dispute Rules	JAMS	April 15, 2024	Data Confidentiality, Secure handling of AI systems/materials (Rule 16.1.b), Limited access to experts, Governing AI-related disputes.
Guide to the Use of Artificial Intelligence in Cases Administered Under the SCC Rules (AI Companion)	Stockholm Chamber of Commerce (SCC)	October 16, 2024	Non-Delegation of Decision-Making, Transparency/Integrity, Confidentiality, Quality Control (flagging AI-generated content), non-binding flexible guidance.
Guidelines for AI Assistance in Court Submissions	Court of King’s Bench in Manitoba, Canada	N/A (Issued considering evolving landscape)	Disclosure of AI assistance in preparation of materials.

Table 3: Institutional Adoption of AI in Arbitration (ICCA Hongkong ’24 Survey Highlights)

Institution Type	Number of Respondents	Number of Institutions Using AI	Primary Areas of AI Use	Specific Examples of AI Application by Institutions
Regional and International Arbitral Institutions	11	4	Internal Processes, Case Management, Enhancement of current dispute resolution services, New Products and services incorporating AI	Employing coders/engineers for case management; AI for drafting awards/documents; Promoting informed AI application; AI-specific working group; Automated scheduling tool; ChatGPT/DeepL for internal transcripts; Issuing guidelines for AI-related disputes.
(Remaining 7 institutions)	7	0 (Interested)	Case Management, Internal Processes	Considering deployment for efficiency improvements.

VI. Case Study: LaPaglia v. Valve Corp. – A Litmus Test for AI in Adjudication

The case of LaPaglia v. Valve Corp. represents a vital moment in the converse girding AI in arbitration, serving as a real- world litmus test for the legal and ethical boundaries of AI’s involvement in adjudicative processes.

Factual Background and Allegations of AI Use

LaPaglia, a consumer of PC games, initiated an arbitration claim against Valve Corp., the owner of the Steam online game store. The descendant sought compensation for alleged antitrust violations and breach of bond related to a imperfect PC game. The disagreement was heard before a sole adjudicator over a 10- day period in December 2024. During breaks in the proceedings, the Arbitrator reportedly mentioned having used ChatGPT to draft a short composition for an aeronautics club, indicating a particular familiarity with and amenability to use AI for drafting to save time. likewise, the Arbitrator allegedly expressed a desire to issue a decision snappily due to a forthcoming trip to the Galapagos islets. The final post-hearing detail was submitted on December 23, 2024, and the 29- runner award was issued remarkably fleetly, just 15 days latterly, on January 7, 2025, purportedly coinciding with the Arbitrator’s listed departure for his trip. On April 8, 2025, the Descendant filed a solicitation to Vacate Arbitration Award before the United States District Court for the Southern District of California. The core allegation was that the Arbitrator had” outsourced his adjudicative part to Artificial Intelligence (‘ AI’)”. The Descendant’s assertion of AI use was supported by several factual rudiments the Arbitrator’s yarn about ChatGPT, his stated urgency to conclude the case before his trip, the presence of” reflective signs of AI generation” within the award itself( including purportedly untrue data not presented at trial or in the record, and lacking applicable citations), and indeed ChatGPT’s own assessment that a paragraph from the award displayed” awkward phrasing, redundancy, reason, and overgeneralization,” suggesting AI authorship. 

Legal Arguments for Vacatur and Counteraccusations for Arbitrator Authority 

The Claimant’s legal arguments for vacatur primarily reckoned on Section 10 (a)(4) of the Federal Arbitration Act (FAA), which permits the vacating of an award if an adjudicator” exceeds their powers” by acting outside the compass of the parties’ contractual agreement. The central contention was that by allegedly counting on AI, the Arbitrator had exceeded his authority, which was contractually bound by the arbitration agreement to give a” neutral adjudicator” and a” written decision” accompanied by a” statement of reasons” for the holding. The Descendant asserted that outsourcing decision- making to AI” betrays the parties’ prospects of a well- reasoned decision rendered by a mortal adjudicator”. This argument drew an analogy to other U.S. cases, similar as Move, Inc. v. Citigroup Global Mkts., where courts vacated awards due to judges falsifying credentials or making other false representations. The Descendant argued that outsourcing decision- making to AI was akin to outsourcing to an” unqualified’ pretender’,” undermining the integrity of the arbitral panel. While the solicitation also cited other grounds for vacatur, similar as taboo connection of claims and turndown to permit an expert report, the AI aspect was a prominent and new argument. 

Analysis in Light of Non-Delegation and Transparency Principles

The LaPaglia case directly highlights the critical question of whether judges should calculate on AI and to what extent, serving as a compelling real- world test for the principle of on-delegation. It underscores the abecedarian demand that judges cannot outsource their adjudicative function to a third party whether mortal or machine and must n’t allow technology to compromise their independent logic. The case brings to the van the significant threat of AI” visions” the generation of inaccurate or false information which, if not strictly reviewed and vindicated by the adjudicator, can gravely compromise the quality and trustability of an award. likewise, the case raises profound questions about translucency whether exposure of AI use is needed, and if so, when and to what extent. This aligns directly with the recommendations arising from colorful guidelines, emphasizing the need for openness when AI tools are employed. Eventually, the LaPaglia case reinforces that judges bear ultimate responsibility for the delicacy, integrity, and mortal authorship of their awards, anyhow of any AI backing. It also spotlights an arising evidentiary issue how can parties prove that an award or part of it was drafted by AI? This raises critical questions about the trustability of AI discovery tools and how courts should treat similar substantiation in unborn controversies. Anyhow of its ultimate judicial outgrowth, LaPaglia v. Valve Corp. stands as a corner case because it’s one of the first, if not the first, to directly challenge an arbitral award grounded on contended over-reliance on AI. 

This case forces the arbitration community and courts to defy the practical, legal, and ethical counteraccusations of AI use in the core adjudicative process, thereby moving the discussion from theoretical enterprises to real- world action. Its elevation ensures that the debate around AI’s part in arbitration will continue with heightened urgency. The Descendant’s central argument that the adjudicator’s contended AI use” betrays the parties’ prospects of a well- reasoned decision rendered by a mortal adjudicator” point to an abecedarian” anticipation gap”. Parties entering arbitration implicitly contract for mortal judgment, and undisclosed AI involvement in core adjudicative tasks potentially violates this foundational understanding. This could come a significant new ground for grueling awards, as the perceived legality of an arbitral award is deeply tied to the belief that it’s the product of mortal intellect and reasoning. However, it strikes at the heart of party autonomy and trust in the arbitral process, creating a important argument for vacatur that extends beyond bare procedural irregularities, If AI is seen to undermine this. 

The Descendant’s reliance on” reflective signs of AI generation” and ChatGPT’s own assessment of the award’s textbook introduces a new and complex evidentiary frontier. This raises critical questions about the trustability and legal admissibility of AI discovery tools and forensic analysis in determining the extent of AI’s influence on legal documents, particularly arbitral awards. However, establishing evidence of reliance becomes a significant chain, If the styles for detecting AI- generated content are themselves fallible or controversial. This highlights a new area of legal and specialized challenge that the LaPaglia case brings to the van, taking courts and interpreters to develop new norms for forensic analysis in this sphere.

VII. AI in Arbitration: The Indian Context

India’s approach to AI in arbitration reflects a broader global trend, characterized by both enthusiastic relinquishment of technology and a conservative station on its nonsupervisory counteraccusations.

Current Regulatory Landscape and Legislative Developments

The Arbitration and Conciliation Act, 1996, which serves as the primary legislation governing arbitration in India, does not explicitly address the use of AI or any other digital tools within arbitration proceedings. This lack of specific legal vittles creates a” nonsupervisory vacuum,” leading to nebulosity regarding the admissibility, trustability, and enforceability of AI- supported arbitration processes and awards. Despite this legislative gap, India is” gradually conforming to the technological revolution”. The Digital Personal Data Protection Act, 2023, has introduced a comprehensive data sequestration governance frame, which is particularly applicable for AI operations handling sensitive arbitration data, icing confidentiality and compliance with data protection morals. likewise, the Indian government indicated in 2018 that the development of specific laws and regulations concerning Artificial Intelligence was in progress. Policy reports, similar as NITI Aayog’s ODR Policy Plan for India (2021), emphasize the transformative eventuality of AI in Online disagreement Resolution (ODR), suggesting that India is laboriously considering a nonsupervisory frame that balances invention with safeguards against abuse, bias, and data vulnerabilities. The NITI Aayog report specifically honored AI’s eventuality in achieving better issues in arbitration, pressing that AI and other technological tools could significantly change the disagreement agreement process by efficiently structuring complicated issues, relating trade- offs, and aiding parties in chancing optimal courses of action.

Table:4 “NITI Aayog’s 2021 ODR Policy proposes a hybrid framework reconciling efficiency and due process:

Tier	Case Value	AI Integration	Oversight Mechanism
1	< ₹5 lakh	Fully automated resolution	Post-hoc judicial review
2	₹5-50 lakh	AI evidence analysis + human award	Arbitrator certification
3	> ₹50 lakh	AI-assisted research only	SVAMC Guideline 6 compliance

Judicial Openness and Initiatives

While the Indian bar has not yet delivered judgments directly ruling on the use of Artificial Intelligence within arbitration, the Indian bar has demonstrated a notable openness to technology. The Supreme Court of India’s establishment of an Artificial Intelligence Committee and the preface of enterprise like the Supreme Court Portal for Assistance in Courts Efficiency (SUPACE) illustrate a judicial amenability to incorporate AI to enhance effectiveness and vacuity. The Supreme Court Vidhik Anuvaada Software (SUVAS) for legal document restatement further underscores this judicial grasp of technology. These court-focused developments, although not directly pertaining to arbitration, set a precedent that can impact the broader relinquishment of AI in disagreement resolution mechanisms across the country.

Applicable Case Laws and Implications

Although direct judgments on AI in Indian arbitration are absent, several landmark cases offer important perspectives on the principles that would likely govern AI’s use:

• SBP & Co. v. Patel Engineering Ltd. (2005)  emphasizes party autonomy and procedural fairness. These principles must be rigorously protected when AI tools are employed to ensure transparency and equity in the arbitral process.   
• ONGC Ltd. v. Western Geco International Ltd. (2014) reiterated the limited scope for judicial interference in arbitral awards, underscoring the significance of clear logic and procedural propriety. Any AI applications in arbitration would need to comply with these standards to withstand legal scrutiny.   
• National Internet Exchange of India v. Reliance Industries Limited & Ors. (2021) demonstrates judicial acceptance of technology-enabled evidence, indicating a readiness to consider AI-generated data and analysis in dispute resolution.   

As India implements the Personal Data Protection Act, 2023, forthcoming arbitration cases are anticipated to address privacy concerns linked with AI systems, which will set critical precedents on data confidentiality in AI-powered arbitration platforms.   

VIII. Conclusion

The primary challenge for AI in arbitration in India is the absence of a comprehensive legal frame that could guard the process with smaller complications. This nonsupervisory void creates query regarding the legal standing and enforceability of AI- supported opinions. Despite this, the eventuality for AI in India is substantial. AI can efficiently structure complicated issues, identify trade- offs, and help parties in chancing optimal courses of action. Technologies like AI can also be gauged up to address multiple controversies coincidently, significantly dwindling the time needed for resolution. The UNCITRAL Convention on Electronic Dispatches in International Contracts, 2007, in its papers 6 and 18, outlines important factors that expand the compass of blockchain contracts and the inflow of electronic data in the arbitration process, furnishing a frame for digital integration. However, it could also significantly strengthen the country’s technological legal frame, If AI is used efficiently in India with a view to expanding arbitration. India’s current situation regarding AI in arbitration nearly glasses the global geography there’s a strong recognition of AI’s eventuality for enhancing effectiveness and reducing case backlogs, but this is coupled with a significant” nonsupervisory vacuum” and patient enterprises about data protection, ethical counteraccusations, and the enforceability of AI- supported processes. This parallel to the global discussion positions India as a crucial case study that demonstrates the universal challenges and openings associated with integrating AI into legal systems worldwide. The Indian Supreme Court’s visionary enterprise, similar as establishing an AI Committee and launching platforms like SUPACE and SUVAS, demonstrate a clear judicial amenability to embrace AI. This stands in discrepancy to the slower pace of legislative development in explicitly regulating AI’s use in arbitration. This difference between judicial proactiveness and legislative pause highlights a crucial dynamic in India’s trip toward AI integration in its legal system. While the bar is pushing the boundaries of technological relinquishment, the legislative frame is still catching up, creating a complex terrain for interpreters navigating AI’s part in disagreement resolution.

Shruti Dyodia
College – O. P JINDAL GLOBAL UNIVERSITY