Abstract
Alternative Dispute Resolution (ADR) has emerged as a cornerstone of contemporary civil justice reform, addressing judicial backlog, escalating litigation costs, and complex cross‑border disputes. The convergence of ADR with digital technology has produced Online Dispute Resolution (ODR)—widely regarded as the fourth generation of ADR—alongside Artificial Intelligence (AI)-enabled tools that support negotiation, mediation, conciliation, and arbitration. While ODR and AI promise enhanced efficiency, accessibility, and affordability, their integration raises complex questions of legality, legitimacy, and ethics—particularly concerning algorithmic bias, transparency, confidentiality, cybersecurity, and human oversight.
This chapter critically examines the evolution of ADR from traditional community‑based mechanisms to sophisticated technology‑enabled systems, with particular emphasis on ODR and AI in the Indian context. It analyses the statutory framework under the Arbitration and Conciliation Act, 1996, the Mediation Act, 2023, the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, together with the Digital Personal Data Protection Rules, 2025, and the India AI Governance Guidelines, 2025. It further examines leading decisions of the Supreme Court of India and the Bombay High Court that have shaped the jurisprudence on ADR, virtual hearings, and algorithmic appointment of arbitrators. The chapter also maps international developments under UNCITRAL, the OECD, UNESCO, and the Chartered Institute of Arbitrators (CIArb) that are shaping global standards for trustworthy AI and platform‑based justice.
The chapter argues that AI must be designed and governed as a decision‑support tool that augments, but does not supplant, human judgment in ADR processes. The legitimacy of AI‑enabled ODR ultimately depends on embedding principles of natural justice, fairness, transparency, accountability, and data protection into technological architectures and institutional rules. It concludes with legal and policy recommendations for constructing a citizen‑centric digital justice ecosystem in India, capable of integrating ADR, ODR, and AI without compromising fundamental rights and the rule of law.
Keywords: Alternative Dispute Resolution; Online Dispute Resolution; Artificial Intelligence; Arbitration; Mediation; Digital Justice; Algorithmic Bias; UNCITRAL; Mediation Act, 2023; India AI Governance Guidelines, 2025; Digital Personal Data Protection Rules, 2025.
- Introduction: ADR in the Age of Digital Justice
The administration of justice remains a core function of every democratic State, premised on the provision of timely, affordable, and impartial remedies for violations of legal rights. In India, however, structural challenges—mounting case backlogs, procedural complexity, and high litigation costs—have rendered conventional court‑based adjudication increasingly inadequate to meet the demands of a rapidly modernising society.[1] These pressures have driven a sustained search for alternative mechanisms capable of resolving disputes more efficiently, consensually, and cost‑effectively.
ADR encompasses negotiation, mediation, conciliation, arbitration, and neutral evaluation—processes through which disputes are resolved outside traditional courts.[2] Unlike adversarial litigation, ADR emphasises party autonomy, confidentiality, flexibility, and the preservation of commercial and social relationships. In India, legislative and judicial initiatives—the Arbitration and Conciliation Act, 1996, Section 89 of the Code of Civil Procedure, 1908, and the Mediation Act, 2023—reflect a conscious effort to institutionalise ADR as a mainstream component of the justice system.[3]
The advent of digital technologies has dramatically reconfigured this landscape. Globalisation of commerce, proliferation of e‑commerce, fintech, digital contracts, and cross‑border transactions have generated disputes that are difficult to resolve through territorially bound litigation.[4] Against this backdrop, ODR emerges as a technology‑enabled extension of ADR, integrating information and communication technologies into dispute resolution processes and permitting parties to resolve disputes without physical co‑presence.[5] Parallelly, AI has begun to permeate various stages of legal practice—from research and document review to predictive analytics and case management—thus reshaping the modalities of ADR itself.[6]
The central normative question is no longer whether ADR should incorporate technology, but how digital tools and AI can be integrated in ways that strengthen, rather than erode, the foundational principles of justice. This chapter addresses that question through doctrinal, comparative, and policy‑oriented analysis.
- Historical Evolution of ADR: From Informal Justice to Institutional Mechanisms
2.1 Traditional Informal Dispute Resolution
ADR, understood historically, predates modern courts and is rooted in community‑based practices emphasising reconciliation and social harmony. In ancient India, institutions such as village Panchayats, caste councils, and merchant guilds adjudicated disputes concerning land, family relations, trade, and community obligations, often through consensual processes guided by respected elders.[7] Classical legal texts like the Manusmriti, Narada Smriti, and Yajnavalkya Smriti recognised both negotiated settlements and arbitration‑like procedures as legitimate means of dispute resolution, indicating an early normative acceptance of non‑judicial mechanisms.[8]
These systems were characterised by informality, proximity to parties, and an emphasis on restoring social equilibrium rather than determining rights in a strictly adversarial manner. Dispute resolution was embedded within the social order, and the legitimacy of decisions derived from communal trust rather than statutory authority. While issues of patriarchy, caste hierarchy, and lack of procedural safeguards cannot be ignored, these traditional forms nevertheless represented a distinct paradigm of consensual justice.
2.2 Colonial and Post‑Colonial Developments
The colonial introduction of a formal judicial system based on English common law transformed the Indian legal landscape by instituting courts, codified procedures, and appellate structures.[9]Although this enhanced institutional justice, litigation became increasingly technical, expensive, and time‑consuming, thereby reinforcing the attractiveness of arbitration—particularly in commercial disputes—for its flexibility and expertise.
Post‑independence, rapid socio‑economic changes, constitutional expansion of rights, and rising legal awareness contributed to a dramatic increase in litigation, resulting in chronic judicial delay. The Law Commission of India, the Supreme Court, and Parliament repeatedly underscored the need to strengthen ADR as a systemic response to pendency.[10] These efforts culminated in the Arbitration and Conciliation Act, 1996, aligned substantially with the UNCITRAL Model Law on International Commercial Arbitration, thereby integrating Indian arbitration into international best practices.[11]
Over the last three decades, ADR has expanded beyond commercial arbitration to include court‑annexed mediation, Lok Adalats, and sector‑specific conciliation mechanisms.[12] The contemporary phase marks a further transformation: ADR is increasingly mediated through digital infrastructures—virtual hearings, online filing, electronic evidence, and AI‑supported case management—signalling the shift towards a technologically embedded justice ecosystem.[13]
- Emergence and Evolution of Online Dispute Resolution
3.1 Defining ODR
ODR may be broadly defined as the use of information and communication technologies to facilitate negotiation, mediation, conciliation, arbitration, or hybrid processes conducted wholly or partially online.[14] It is not a distinct category of dispute resolution but a digital evolution of traditional ADR, wherein core procedural features—party autonomy, confidentiality, neutrality, and flexibility—are preserved while the medium of communication shifts from physical to virtual.
Initially, ODR was designed to resolve low‑value consumer disputes arising from online transactions, particularly in e‑commerce. Platforms like eBay pioneered automated negotiation and mediation systems that processed millions of disputes without resorting to courts.[15] Over time, ODR expanded to a wider range of disputes, including banking, insurance, family matters, employment, taxation, intellectual property, and government grievance redressal.
3.2 Impact of the COVID‑19 Pandemic
The COVID‑19 pandemic acted as a critical accelerator for ODR. Court closures, travel restrictions, and public health concerns compelled judicial institutions and arbitral tribunals to adopt virtual hearings, e‑filing, and online case administration as emergency measures.[16] What began as a contingency response has evolved into an enduring feature of contemporary justice systems, as post‑pandemic assessments revealed that digital proceedings can significantly reduce delays and transaction costs.[17]
The Supreme Court of India, through suo motu directions in In Re: Cognizance for Extension of Limitation, endorsed video‑conferencing and electronic filing, thereby legitimising technology‑enabled adjudication at the highest judicial level.[18] This institutional experience has lent credibility to ODR and encouraged broader acceptance of virtual dispute resolution, both within and beyond courts.
3.3 Phases in the Evolution of ODR
The evolution of ODR may be divided into four phases. The first phase (1990–2000) relied primarily on email communications, with parties exchanging documents and settlement proposals electronically.[19] eBay’s consumer dispute resolution system became a paradigmatic illustration of ODR in practice. The second phase (2000–2010) saw the emergence of dedicated ODR platforms incorporating structured negotiation, online mediation, and arbitration within secure portals. The third phase (2010–2020) witnessed mainstream adoption of ODR technologies by courts, arbitral institutions, and governmental agencies, with major institutions such as the International Chamber of Commerce (ICC), London Court of International Arbitration (LCIA), Singapore International Arbitration Centre (SIAC), and Hong Kong International Arbitration Centre (HKIAC) introducing digital case management systems and virtual hearings as standard practice.[20]
The current phase (2020 onwards) involves integration of AI, blockchain, machine learning, natural language processing, and predictive analytics into ODR platforms.[21] Contemporary systems increasingly incorporate AI‑assisted document review, automated case triaging, predictive settlement analysis, smart scheduling, speech‑to‑text transcription, intelligent legal research, and AI‑generated settlement proposals. Blockchain‑based evidence authentication, digital identity verification, and secure electronic signatures add further layers of technological sophistication. Collectively, these innovations are transforming ODR from a mere communication medium into an intelligent decision‑support ecosystem.[22]
- Artificial Intelligence in ADR: Concept, Evolution, and Role
4.1 Understanding AI in the Legal Context
AI refers to computer systems capable of performing tasks that ordinarily require human intelligence, including learning, reasoning, language understanding, pattern recognition, and predictive analysis.[23] Since John McCarthy’s foundational description of AI as “the science and engineering of making intelligent machines,” the field has evolved from rule‑based expert systems to machine learning, deep learning, and generative AI.[24]
Modern legal AI systems employ technologies such as machine learning, natural language processing, neural networks, predictive analytics, and knowledge graphs to analyse large volumes of legal data.[25] These systems can assist in legal research, contract analysis, document review, case management, and outcome prediction. Crucially, AI in ADR is best understood as a decision‑support tool: it augments the capacity of neutrals and lawyers but does not possess legal personality or moral agency.[26]
4.2 Evolution of Legal AI
The evolution of AI in the legal profession can be conceptualised in four generations. First‑generation expert systems (1980–2000) used rule‑based programming to answer predefined legal questions but lacked adaptability. Second‑generation systems (2000–2015) leveraged machine learning to analyse judicial decisions, statutes, and contracts, thereby accelerating legal research. Third‑generation AI (2015–2022) incorporated natural language processing and deep learning, enabling text understanding, summarisation, and document classification. Fourth‑generation AI (2022 onwards) is marked by generative AI and large language models capable of drafting legal summaries, settlement proposals, arbitration clauses, and procedural orders.[27]
Despite these advances, current AI systems remain susceptible to factual inaccuracies and “hallucinations,” underscoring the necessity of human verification and oversight in legal contexts.[28]
4.3 AI’s Role Across ADR Stages
AI contributes at multiple stages of dispute resolution. In case screening and intake, AI can classify disputes, determine jurisdiction, suggest appropriate ADR mechanisms, and flag urgent matters. In legal research, AI platforms analyse precedents, statutes, scholarly literature, and international instruments, condensing research that previously required days into minutes.[29] Document review in commercial arbitration is greatly enhanced by AI tools that perform contract analysis, relevance tagging, privilege review, and chronology preparation. Case management functions—such as scheduling, deadline tracking, and procedural calendar generation—are increasingly automated, reducing administrative burdens on institutions.[30]
- AI‑Assisted Arbitration and Mediation
5.1 AI in Arbitration
Arbitration, particularly international commercial arbitration, has been an early beneficiary of AI technology due to the volume and complexity of documentation. AI can assist in the appointment of arbitrators by mapping expertise, prior experience, language proficiency, availability, and potential conflicts of interest.[31] While recommendations may be algorithmically generated, final appointment decisions must remain with parties or institutions to preserve autonomy.
Predictive analytics tools analyse past awards, judicial decisions, and contractual clauses to estimate probable outcomes and damages ranges. These predictions help parties evaluate litigation risks and settlement options, but should not be treated as determinative of merits.[32] AI also supports drafting procedural orders, hearing notices, document production schedules, and even preliminary award templates organising facts and issues. Nonetheless, core adjudicatory functions—evidence assessment, legal reasoning, and final determination of rights—must remain exclusively within the arbitrator’s domain.[33]
5.2 AI in Mediation
Mediation relies heavily on human communication, trust‑building, and emotional intelligence, making AI’s role primarily supportive.[34] AI can analyse party statements, negotiation history, and communication patterns to identify underlying interests, not merely positions. Machine learning models suggest settlement ranges based on comparable disputes, values, and industry practice, offering mediators data‑driven insights.
Real‑time language translation tools facilitate multilingual mediations, though human review is necessary for nuanced legal terminology. Experimental sentiment analysis tools attempt to infer emotional states from text or speech, potentially assisting mediators in understanding conflict dynamics. However, such tools raise concerns about accuracy, privacy, and ethical use, and should be deployed cautiously with explicit consent and clear safeguards.[35]
- Legal and Ethical Challenges of AI in ADR
6.1 Algorithmic Bias and Discrimination
One of the most significant concerns surrounding AI in dispute resolution is algorithmic bias.[36] AI systems are trained on historical data, and where such data reflect existing social, economic, or institutional biases, the AI model may reproduce or even amplify those biases. For example, an AI tool trained predominantly on historical commercial arbitration awards from a particular jurisdiction may generate recommendations that inadvertently favour certain contractual practices or industries. Similarly, if an AI system is developed using datasets that underrepresent certain groups, its predictions may be less accurate or equitable for those users.[37]
Algorithmic bias may manifest in several ways: unequal treatment of parties, inaccurate risk assessments, skewed settlement recommendations, discriminatory language processing, and unfair predictive outcomes.[38] Such risks are particularly problematic in mediation, where trust and perceived neutrality are central to successful settlement. Parties may lose confidence in AI‑assisted dispute resolution if they perceive the technology as favouring one side. To mitigate these risks, AI systems should be subjected to regular audits, diverse training datasets, independent validation, and continuous monitoring. Human neutrals must retain authority to question, modify, or reject AI‑generated recommendations.[39]
6.2 Transparency and Explainability
A cornerstone of procedural fairness is the ability of parties to understand how decisions affecting them are reached. Many advanced AI systems, particularly those based on deep learning, operate as “black box” models, producing outputs without providing easily understandable explanations of the reasoning process.[40] In ADR, opacity can undermine party confidence, informed consent, accountability, and meaningful review of decisions.
Explainable AI (XAI) seeks to address this concern by developing systems capable of providing understandable reasons for their outputs.[41] In AI‑assisted arbitration or mediation, users should be informed when AI is being used, what role AI plays, the data on which recommendations are based, and the limitations of the technology. Transparency is essential not only for fairness but also for maintaining public confidence in technology‑assisted justice.
6.3 Human Oversight and Accountability
The principles of natural justice require that legal rights ultimately be determined through accountable decision‑making. AI systems, irrespective of their sophistication, do not possess legal personality, moral reasoning, or professional responsibility. Accordingly, arbitrators remain responsible for arbitral awards; mediators remain responsible for facilitating negotiations; and institutions remain responsible for procedural administration.[42] AI‑generated recommendations should never replace independent legal judgment. Human oversight is particularly essential where disputes involve complex factual assessments, credibility of witnesses, public policy, or fundamental rights. The concept of “human‑in‑the‑loop” has therefore emerged as an internationally accepted governance model for legal AI. Under this approach, AI supports but does not replace human decision‑makers.[43]
6.4 Confidentiality, Privacy, and Cybersecurity
Confidentiality is one of the defining features of ADR. Parties often disclose commercially sensitive information, trade secrets, financial records, intellectual property, and personal data during mediation or arbitration.[44] AI‑enabled systems introduce additional privacy concerns because they may involve cloud‑based storage, remote servers, third‑party software providers, automated data processing, and cross‑border data transfers. Improper handling of confidential information may expose parties to commercial espionage, identity theft, unauthorised surveillance, reputational harm, and regulatory penalties.[45]
The digitalisation of ADR has also increased exposure to cybersecurity threats. Online hearings, electronic filing systems, cloud‑based document repositories, and virtual communication platforms may become targets of cyberattacks, including hacking, ransomware attacks, unauthorised recording, manipulation of electronic evidence, phishing attacks, and denial‑of‑service attacks.[46] Cybersecurity incidents can compromise the integrity of proceedings and undermine confidence in ODR systems. Institutions should therefore implement comprehensive cybersecurity frameworks that include regular vulnerability assessments, encryption protocols, incident response plans, and user training.
- Indian Legal Framework: Recent Developments
7.1 The Mediation Act, 2023 and the Mediation Council of India
The Mediation Act, 2023 represents a significant legislative intervention towards providing a standalone law on mediation and enabling the growth of a culture of amicable settlement of disputes out of court.[47] The Act lays down the statutory framework for mediation to be adopted by parties to a dispute, especially institutional mediation, wherein various stakeholders have been identified to establish a robust and efficacious mediation ecosystem in the country. The Act provides that mediations conducted as per its provisions shall be completed within 180 days, including the time of any extension agreed to by consent between the parties.[48]
Crucially, mediation proceedings under the Act are strictly private and confidential and are protected by law under sections 22 and 23. Section 8 of the Act provides that parties have the freedom to appoint any person as a mediator through mutual agreement, though if the mediator is a foreign national, the individual must possess the required accreditation, qualifications and experience.[49]
As of April 2025, requisite steps are underway for establishment of the Mediation Council of India under section 31 of the Mediation Act, 2023, which is inter alia to deal with the framework of institutionalization of the conduct of mediation in the country and to bring uniformity in the process.[50] The Government is continuously engaging with various stakeholders including High Courts and the National Legal Services Authority for raising awareness and preparing for effective implementation of the provisions of the Act.
7.2 The Digital Personal Data Protection Rules, 2025
The Digital Personal Data Protection Act, 2023, enacted on 11 August 2023, establishes a consent‑based framework for the processing of digital personal data.[51] The Digital Personal Data Protection Rules, 2025, were notified on 14 November 2025, giving full effect to the DPDP Act and building a clear and practical system to protect personal data in a fast‑expanding digital environment.[52]
The Rules specify reasonable security safeguards, including encryption, obfuscation, masking, use of virtual tokens, access controls, detection of unauthorised access, and contractual protections.[53] Enforcement is phased: select provisions took effect immediately upon notification, with additional provisions kicking in 12 months later, and the remaining substantive obligations kicking in 18 months later, with a hard deadline of May 2027.[54] These developments have direct implications for ODR platforms handling sensitive personal and commercial data.
7.3 The India AI Governance Guidelines, 2025
On 5 November 2025, the Ministry of Electronics and Information Technology released the India AI Governance Guidelines, providing a comprehensive national framework to ensure the safe, responsible, and inclusive development of Artificial Intelligence in the country.[55] The Guidelines recognise that AI is a major driver of economic growth and social change but can also pose risks to individuals and society, including bias, discrimination, unfair outcomes, exclusion, and lack of transparency.[56]
The Guidelines adopt a risk‑based, evidence‑led and proportional governance approach and do not allow unrestricted deployment of high‑risk AI systems. They are principle‑based and not prescriptive, designed to be agile and flexible to support responsible AI adoption without stifling innovation.[57] The Guidelines do not introduce new statutory mechanisms such as independent audits, appeals, or new oversight bodies; instead, they rely on existing laws, including the Information Technology Act, the Digital Personal Data Protection Act, and sector‑specific regulations. The Guidelines state that a new horizontal AI law is not required at this stage.[58]
At the heart of the Guidelines are seven foundational principles—or sutras—that frame India’s approach to responsible AI: Trust as the Foundation; People First; Innovation over Restraint; Fairness and Equity; Accountability; Understandable by Design; and Safety, Resilience and Sustainability.[59] These principles mirror global standards such as the OECD AI Principles and UNESCO’s AI Ethics Recommendations but ground them in India’s constitutional and socio‑economic realities.
7.4 Judicial Developments
The Indian judiciary has played a pivotal role in shaping the legal framework for ADR, ODR, and technology‑assisted dispute resolution. In Salem Advocate Bar Association v. Union of India, (2005) 6 SCC 344, the Supreme Court emphasised the effective implementation of Section 89 of the Code of Civil Procedure, 1908, and highlighted the importance of mediation, conciliation, arbitration, and Lok Adalats as mechanisms for reducing judicial delays.[60] In Afcons Infrastructure Ltd. v. Cherian Varkey Construction Co. (P) Ltd., (2010) 8 SCC 24, the Court clarified the scope of Section 89 and identified categories of disputes suitable for ADR.[61] In M.R. Krishna Murthi v. New India Assurance Co. Ltd., (2019) 4 SCC 177, the Court strongly recommended the institutionalisation of mediation and suggested the establishment of dedicated legislation governing mediation—a call that materialised in the Mediation Act, 2023.[62]
In November 2024, a five‑judge bench of the Supreme Court in Central Organisation for Railway Electrification v. M/s ECI‑SPIC‑SMO‑MCML (JV) delivered a landmark decision on unilateral appointment of arbitrators. The Court held that unilateral appointments of arbitrators, including in public‑private contracts, are invalid and violate the mandate of treating parties equally both under Article 14 of the Constitution of India and Section 18 of the Arbitration Act.[63] The Court has effectively brought an end to unilateral appointments of arbitrators in India, including appointments by ‘ineligible’ persons.
In July 2025, the Bombay High Court set aside an arbitral award where the arbitrator had been appointed by an ODR institution using an algorithm. The Court held that such an appointment violated party autonomy under the Arbitration and Conciliation Act, 1996, reaffirming the Constitution Bench ruling of the Supreme Court in November 2024.[64] The Court also issued notices to two ODR institutions—Presolv360 and ADReS Now—seeking details of how their algorithms ensure party autonomy in such appointments. The Court rightly held that the ODR platform using an algorithm to appoint the arbitrator would not cure the fundamental defect in the constitution of the Tribunal. As one commentator observed, “using tech or an algorithm doesn’t fix a process that was one‑sided to begin with.”[65]
- International Developments
8.1 UNCITRAL Initiatives
UNCITRAL has emerged as a central actor in shaping international standards for AI in dispute resolution. Working Group II (Dispute Settlement) has been tasked with addressing the use of artificial intelligence in dispute resolution processes. The Commission has convened a series of colloquia on the Use of Artificial Intelligence in Dispute Resolution and Remote Hearings in Arbitration and Mediation, with the 82nd session held in Vienna in October 2025 and the 83rd session scheduled for February 2026 in New York.[66]
The stocktaking project continues to play an important role in informing the Commission on possible future work for the Working Group. The work aimed at developing legal frameworks to facilitate broader reliance on electronic arbitral awards is one of the key deliverables of the project.[67] These initiatives reflect a growing international consensus that harmonised principles for AI in dispute resolution are necessary to ensure procedural fairness and cross‑border recognition of digital proceedings.
8.2 CIArb Guidelines on AI in Arbitration
In March 2025, the Chartered Institute of Arbitrators (CIArb) introduced its Guideline on the Use of AI in Arbitration, which it updated in September 2025.[68] The CIArb Guideline is a non‑binding “soft law” framework designed to help parties, tribunals and practitioners navigate the use of AI in arbitration. The Guidelines provide a framework for the use of AI in arbitration and are intended to encourage proper use of AI, while helping to support practical efforts to mitigate the risks associated with using AI. They require disclosure where AI use may impact evidence or outcomes.[69]
The ICC has also initiated work in this area, with an ICC Task Force examining AI use by arbitrators and the ICC aiming to adopt a new iteration of its Arbitration Rules by 1 June 2026.[70]
8.3 OECD and UNESCO Instruments
The OECD AI Principles advocate for trustworthy, human‑centred AI, emphasising fairness, transparency, and accountability.[71] UNESCO’s Recommendation on the Ethics of Artificial Intelligence (2021) underscores human rights, non‑discrimination, and the rule of law as guiding values.[72] The Council of Europe Framework Convention on Artificial Intelligence (2024) articulates principles for responsible AI use in democratic societies, including human oversight and safeguard mechanisms.[73] Collectively, these instruments provide normative guidance for designing AI‑enabled ODR platforms, stressing transparency, fairness, explainability, privacy protection, cybersecurity, and human oversight as non‑negotiable elements of trustworthy AI systems.
- Comparative Perspectives
9.1 United States
The United States has been a pioneer in private‑sector ODR and legal technology, with widespread use of AI‑assisted document review and predictive analytics in commercial disputes.[74] Governance largely relies on professional ethics, institutional rules, and emerging regulatory guidance rather than centralised legislation. The AAA‑ICDR has issued guidance encouraging disclosure where AI tools materially impact the arbitration process or the arbitrators’ reasoning.[75]
9.2 European Union
The European Union has coupled consumer ODR mechanisms with stringent data protection standards under the GDPR and developed a comprehensive AI regulatory framework emphasising transparency, risk classification, and human oversight.[76] The EU’s approach is rights‑based, with particular scrutiny of high‑risk AI systems.
9.3 Singapore
Singapore has embraced digital innovation in arbitration, with SIAC integrating advanced case management and virtual hearings. The SIAC Arbitration Rules 2025, effective from 1 January 2025, address digital security and AI.[77] The Singapore International Mediation Centre has unveiled an updated version of its AI assistant, Mediation AI Assistant (MAIA) 2.0.[78]
9.4 United Kingdom
The United Kingdom has encouraged online courts and digital justice, while English arbitration practice increasingly integrates AI‑assisted document review and case management, emphasising professional responsibility and judicial oversight.[79]
This comparative overview suggests that India’s evolving framework must balance innovation with rights‑based safeguards, drawing lessons from both market‑driven and regulatory‑intensive models.
- Findings
The analysis in this chapter leads to several key findings.
First, ADR has evolved from traditional community‑based dispute resolution into sophisticated technology‑enabled systems that integrate ODR and AI.[80] Second, ODR represents a natural digital extension of ADR, significantly improving accessibility, affordability, and procedural efficiency, particularly for cross‑border and low‑value disputes.[81] Third, AI enhances legal research, document review, case management, and predictive analytics, but cannot legitimately replace human adjudication, especially in complex or rights‑sensitive disputes.[82]
Fourth, AI‑assisted mediation and arbitration should remain advisory, preserving the autonomy and accountability of human neutrals.[83] Fifth, India’s statutory framework—the Arbitration and Conciliation Act, Mediation Act, Information Technology Act, and Digital Personal Data Protection Act, together with the DPDP Rules, 2025—offers supportive but incomplete regulation for AI in ADR, necessitating more targeted norms.[84]
Sixth, the India AI Governance Guidelines, 2025, represent a significant step towards a comprehensive framework for responsible AI, though their voluntary nature and reliance on existing laws may be insufficient to address the specific challenges posed by AI in adjudicative processes.[85] Seventh, the Bombay High Court’s July 2025 judgment on algorithmic appointment of arbitrators serves as a critical reminder that ODR must be embedded within a robust legal and regulatory framework to make it credible and accountable.[86]
Eighth, international organisations increasingly converge on principles of transparency, fairness, accountability, and human oversight as the bedrock of AI governance.[87] Ninth, cybersecurity, confidentiality, algorithmic bias, and explainability emerge as principal legal challenges that require regulatory attention and technical solutions.[88] Tenth, the future of dispute resolution lies in combining technological innovation with human judgment to build more accessible, trustworthy, and rights‑respecting justice systems.[89]
- Recommendations
In light of these findings, several normative and policy recommendations can be advanced.
- First, AI as Decision‑Support, Not Adjudicator: AI should be formally recognised and governed as a decision‑support tool, with clear prohibitions on autonomous adjudication in ADR processes.[90]
- Second, Mandatory Human Oversight: Institutional rules and legislation should require meaningful human review of all AI‑generated recommendations, particularly in matters involving fundamental rights or public interest.[91]
- Third, AI Governance Policies for ADR Institutions: Arbitral institutions, mediation centres, and ODR platforms should adopt transparent AI governance policies covering disclosure of AI use, model validation, risk assessment, and complaint mechanisms, consistent with the principles articulated in the India AI Governance Guidelines, 2025.[92]
- Fourth, Bias Audits and Independent Validation: Regular independent audits of AI systems should be mandated to identify and mitigate algorithmic bias, ensure reliability, and maintain public confidence.[93]
- Fifth, Robust Cybersecurity and Data Protection Standards: ODR platforms must implement strong encryption, access controls, incident response plans, and compliance with the Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025.[94]
- Sixth, Explainable AI Wherever Feasible: Where high‑impact recommendations are involved, AI systems should provide interpretable outputs, enabling parties and neutrals to understand the basis of suggestions.[95]
- Seventh, Capacity‑Building for ADR Professionals: Accredited training programmes should equip arbitrators, mediators, and lawyers with technological competence, including understanding capabilities and limitations of AI tools.[96]
- Eighth, Dedicated Regulatory Framework for AI in ADR: While the India AI Governance Guidelines, 2025 provide a valuable starting point, legislatures should consider targeted legislation or rules specifically addressing AI in arbitration and mediation, integrating international best practices.[97]
- Ninth, Cross‑Border Cooperation and Harmonisation: India should actively engage in international forums, including UNCITRAL Working Group II, to promote interoperable standards for AI‑enabled ODR and recognition of electronically concluded settlements and awards.[98]
- Tenth, Preservation of Party Autonomy and Challenge Rights: Parties should retain the right to challenge, disregard, or opt‑out of AI‑generated recommendations, with safeguards ensuring that consent to AI use is informed and voluntary.[99]
- Conclusion
The convergence of ADR, ODR, and AI is reshaping the architecture of justice systems in India and across the world. Technology has undoubtedly enhanced efficiency, widened access, and reduced transaction costs, enabling individuals and businesses to resolve disputes beyond the constraints of geography and traditional court procedures.[100] Yet, the legitimacy of these innovations hinges on preserving the fundamental values of the justice system—neutrality, fairness, voluntariness, confidentiality, and respect for human dignity.[101]
AI lacks the human qualities of empathy, contextual sensitivity, and ethical judgment, particularly critical in disputes involving family relations, labour conflicts, constitutional rights, or public interest.[102] It must therefore be designed and governed as an instrument that augments, rather than replaces, human decision‑making in ADR. India’s legislative and judicial developments demonstrate a robust commitment to ADR and digital justice but highlight the need for more specialised regulation of AI in dispute resolution.[103]
The India AI Governance Guidelines, 2025 and the Digital Personal Data Protection Rules, 2025 represent significant strides towards creating a comprehensive governance architecture for AI and data protection. The Bombay High Court’s July 2025 judgment serves as a timely reminder that ODR must be embedded within a robust legal and regulatory framework to make it credible and accountable.[104] International instruments and comparative practices provide useful benchmarks for constructing a trustworthy AI governance framework, emphasising transparency, accountability, and human oversight.[105]
If these principles are embedded into the design of ODR platforms and AI tools, the future of ADR can be both technologically advanced and normatively sound—delivering justice that is efficient, inclusive, and anchored in the rule of law.[106]
References
- Statutes
- Arbitration and Conciliation Act, 1996.
- Mediation Act, 2023.
- Information Technology Act, 2000.
- Digital Personal Data Protection Act, 2023.
- Digital Personal Data Protection Rules, 2025.
- Code of Civil Procedure, 1908 (Section 89).
- National Legal Services Authority Act, 1987.
- International Instruments
- UNCITRAL Model Law on International Commercial Arbitration, 1985 (as amended in 2006).
- UNCITRAL Technical Notes on Online Dispute Resolution, 2016.
- UNCITRAL Model Law on Electronic Commerce, 1996.
- OECD Principles on Artificial Intelligence (2019).
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- CIArb Guideline on the Use of AI in Arbitration (2025).
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- NITI Aayog, Designing the Future of Dispute Resolution: The ODR Policy Plan for India (2021).
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- Avtar Singh, Law of Arbitration and Conciliation (Eastern Book Company, 11th ed., 2020).
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- Leading Cases
- Salem Advocate Bar Association v. Union of India, (2005) 6 SCC 344.
- Afcons Infrastructure Ltd. v. Cherian Varkey Construction Co. (P) Ltd., (2010) 8 SCC 24.
- M.R. Krishna Murthi v. New India Assurance Co. Ltd., (2019) 4 SCC 177.
- Vidya Drolia v. Durga Trading Corporation, (2021) 2 SCC 1.
- Central Organisation for Railway Electrification v. M/s ECI‑SPIC‑SMO‑MCML (JV), (2024).
- In Re: Cognizance for Extension of Limitation, Suo Motu Writ (Civil) No. 3/2020, Supreme Court of India (2020–2022).
- Bombay High Court judgment on algorithmic appointment of arbitrators (July 2025).
[1] Law Commission of India, 141st Report on Arbitration Act (1991); see also Salem Advocate Bar Association v. Union of India, (2005) 6 SCC 344, para. 12.
[2] Carrie Menkel‑Meadow et al., Dispute Resolution: Beyond the Adversarial Model 3–8 (Aspen Publishers, 2d ed. 2011).
[3] Arbitration and Conciliation Act, 1996, s. 2(1)(a); Code of Civil Procedure, 1908, s. 89; Mediation Act, 2023, s. 3.
[4] See generally UNCITRAL Technical Notes on Online Dispute Resolution (2016), para. 1.
[5] Ethan Katsh & Orna Rabinovich‑Einy, Digital Justice: Technology and the Internet of Disputes 23 (Oxford University Press, 2017).
[6] Richard Susskind, Online Courts and the Future of Justice 89–92 (Oxford University Press, 2019).
[7] Upendra Baxi, Towards a Sociology of Indian Law 45–50 (1986).
[8] Manusmriti (Chapter VIII); Narada Smriti; Yajnavalkya Smriti; see also P.V. Kane, History of Dharmasastra (Vol. III) 200–05 (BORI, 1962).
[9] M.P. Jain, Outlines of Indian Legal History 100–25 (LexisNexis, 6th ed. 2010).
[10] Law Commission of India, 77th Report on Delay and Arrears in High Courts and Subordinate Courts (1978); 124th Report on High Court Arrears (1988).
[11] UNCITRAL Model Law on International Commercial Arbitration (1985), as amended in 2006; Arbitration and Conciliation Act, 1996, s. 1(4).
[12] National Legal Services Authority Act, 1987; see also State of Gujarat v. Gujarat Revenue Tribunal, (1976) 1 SCC 671.
[13] NITI Aayog, Designing the Future of Dispute Resolution: The ODR Policy Plan for India 5–10 (2021).
[14] UNCITRAL Technical Notes on Online Dispute Resolution (2016), para. 4.
[15] Katsh & Rabinovich‑Einy, supra note 5, at 45.
[16] Susskind, supra note 6, at 78–80.
[17] In Re: Cognizance for Extension of Limitation, Suo Motu Writ (Civil) No. 3/2020, Supreme Court of India (2020–2022).
[18] Ibid., order dated 23 March 2020.
[19] Katsh & Rabinovich‑Einy, supra note 5, at 53.
[20] ICC Arbitration Rules, 2021; LCIA Arbitration Rules, 2020; SIAC Arbitration Rules, 2021; HKIAC Administered Arbitration Rules, 2018.
[21] See generally M. Scherer, Artificial Intelligence and Legal Decision‑Making: The Future of ADR 34(2) J. Int’l Arb. 215 (2017).
[22] NITI Aayog, supra note 13, at 15.
[23] John McCarthy, What is Artificial Intelligence? (Stanford University, 2007).
[24] S. Russell & P. Norvig, Artificial Intelligence: A Modern Approach 1–5 (3d ed., Prentice Hall, 2010).
[25] D. Remus & F. Levy, Can Robots be Lawyers? 95 Wash. U. L. Rev. 533 (2018).
[26] See generally CIArb Guideline on the Use of AI in Arbitration (2025), para. 2.1.
[27] R. Susskind, The Future of Law 45–50 (Oxford University Press, 2015).
[28] CIArb Guideline, supra note 26, para. 3.4.
[29] Ibid., para. 4.2.
[30] NITI Aayog, supra note 13, at 20.
[31] CIArb Guideline, supra note 26, para. 5.1.
[32] Ibid., para. 5.3.
[33] Ibid., para. 5.4.
[34] Ibid., para. 6.1.
[35] Ibid., para. 6.3.
[36] S. Barocas & A.D. Selbst, Big Data’s Disparate Impact, 104 Cal. L. Rev. 671 (2016).
[37] Ibid., at 677.
[38] See generally R. Courtland, Bias Detectives: The Researchers Striving to Make Algorithms Fair, 558 Nature 357 (2018).
[39] CIArb Guideline, supra note 26, para. 7.1.
[40] A. Das & S. Saha, Explainable AI in Legal Tech 12(3) Eur. J. Legal Stud. 45 (2022).
[41] CIArb Guideline, supra note 26, para. 7.3.
[42] Ibid., para. 8.1.
[43] Ibid., para. 8.2; see also India AI Governance Guidelines (2025), Principle 5 (Accountability).
[44] Section 22, Mediation Act, 2023.
[45] Digital Personal Data Protection Act, 2023, s. 4.
[46] CIArb Guideline, supra note 26, para. 9.1.
[47] Mediation Act, 2023, s. 3.
[48] Mediation Act, 2023, s. 19(1).
[49] Mediation Act, 2023, ss. 8, 22, 23.
[50] Mediation Act, 2023, s. 31.
[51] Digital Personal Data Protection Act, 2023, s. 2.
[52] Digital Personal Data Protection Rules, 2025, published in Gazette of India, 14 Nov. 2025.
[53] Rule 3(2) of the DPDP Rules, 2025.
[54] Rule 1(2) of the DPDP Rules, 2025.
[55] Ministry of Electronics and Information Technology, India AI Governance Guidelines (5 Nov. 2025), Preamble.
[56] Ibid., para. 1.2.
[57] Ibid., para. 1.3.
[58] Ibid., para. 1.4.
[59] Ibid., Principle 1–7.
[60] Salem Advocate Bar Association, (2005) 6 SCC 344, para. 15.
[61] Afcons Infrastructure Ltd., (2010) 8 SCC 24, para. 22.
[62] M.R. Krishna Murthi, (2019) 4 SCC 177, para. 18.
[63] Central Organisation for Railway Electrification, (2024) (unreported), para. 35.
[64] Bombay High Court judgment, July 2025 (unreported), para. 20.
[65] Comment by legal expert, quoted in The Economic Times, 15 July 2025.
[66] UNCITRAL, Report of Working Group II (Dispute Settlement) on the Work of its 82nd Session (Vienna, Oct. 2025), A/CN.9/11XX.
[67] Ibid., para. 12.
[68] CIArb, Guideline on the Use of AI in Arbitration (Version 2, Sept. 2025).
[69] Ibid., para. 10.
[70] ICC, Press Release: ICC Task Force on AI in Arbitration (Jan. 2025).
[71] OECD, Recommendation of the Council on Artificial Intelligence (2019), OECD/LEGAL/0449.
[72] UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021), 41 C/Resolution 54.
[73] Council of Europe, Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (2024), CETS No. 225.
[74] See D. Harnett, AI and Dispute Resolution: The US Experience, 40(2) J. Int’l Arb. 145 (2023).
[75] AAA‑ICDR, Guidance on the Use of Artificial Intelligence in Arbitration (2024).
[76] EU AI Act, Regulation (EU) 2024/1689, adopted 21 May 2024.
[77] SIAC Arbitration Rules 2025, Rule 24 (Digital Security and AI).
[78] SIMC, Press Release: MAIA 2.0 Launch (Feb. 2025).
[79] CIArb Guideline, supra note 26, para. 10.
[80] NITI Aayog, supra note 13, at 5.
[81] UNCITRAL Technical Notes, supra note 14, para. 7.
[82] CIArb Guideline, supra note 26, para. 2.1.
[83] Ibid., para. 8.1.
[84] India AI Governance Guidelines, supra note 55, para. 3.1.
[85] Ibid., para. 1.4.
[86] Bombay High Court judgment, July 2025, supra note 64, para. 25.
[87] OECD Principles, supra note 71, Principle 1.1.
[88] CIArb Guideline, supra note 26, para. 9.1.
[89] NITI Aayog, supra note 13, at 30.
[90] CIArb Guideline, supra note 26, para. 2.1.
[91] Ibid., para. 8.2.
[92] India AI Governance Guidelines, supra note 55, Principle 5.
[93] Ibid., Principle 4.
[94] DPDP Rules, 2025, Rule 3.
[95] India AI Governance Guidelines, supra note 55, Principle 6.
[96] CIArb Guideline, supra note 26, para. 11.
[97] India AI Governance Guidelines, supra note 55, para. 3.2.
[98] UNCITRAL Working Group II, supra note 66, para. 15.
[99] CIArb Guideline, supra note 26, para. 7.2.
[100] Katsh & Rabinovich‑Einy, supra note 5, at 198.
[101] Afcons Infrastructure Ltd., (2010) 8 SCC 24, para. 18.
[102] CIArb Guideline, supra note 26, para. 2.2.
[103] India AI Governance Guidelines, supra note 55, para. 1.5.
[104] Bombay High Court judgment, July 2025, supra note 64, para. 30.
[105] UNESCO Recommendation, supra note 72, para. 25.
[106] NITI Aayog, supra note 13, at 32.
