Online Resolution Mechanism In The Indian Judicial System: An Exploratory Study
Bibliographic record
Abstract
The swift digital transition in governance and public institutions has profoundly impacted the Indian judicial system, positioning Online Resolution Mechanisms (ORMs) as a burgeoning frontier in legal reform. This exploratory study analyses the conceptual framework, evolution, efficacy, and obstacles associated with the integration of online platforms into dispute resolution processes in India. With cases piling up, delays, high expenses of litigation, and limited access to courts, ORMs could be a step towards a more efficient, citizen-focused, and technology-driven way to dispense justice. The study places ORMs in the context of India's larger digital governance ecosystem, especially programs like Digital India, the e-Courts Mission Mode Project, the National Judicial Data Grid (NJDG), and the use of virtual courts, e-filing, e-payment, and video conferencing systems during and after the COVID-19 pandemic. These new ideas show that the courts are more eager to use digital tools to make the process more efficient, open, and convenient for the public. The study article examines the characteristics and extent of Online Resolution Mechanisms (ORMs), including Online Dispute Resolution (ODR), virtual hearings, electronic documentation, AI-assisted tools, and automated processes utilised by courts, tribunals, and quasi-judicial entities. It looks into the statutory changes, judicial decisions, regulatory frameworks, and policy reports that affect how ORMs work in India through doctrinal analysis. The report underscores the growing reliance on ODR mechanisms—specifically mediation, arbitration, and negotiation facilitated through digital interfaces—in commercial disputes, consumer grievances, e-commerce transactions, and micro-finance issues. It also looks at how the Supreme Court's support for digital courts and the suggestions of committees led by Justice D.Y. Chandrachud and NITI Aayog have helped create an atmosphere that is good for ODR. A fundamental aspect of the study is the assessment of ORM's efficacy in facilitating access to justice. There is a lot of talk about important indicators like lower case backlogs, lower costs, simpler procedures, more user satisfaction, and more geographical coverage. The report points out that ORMs have helped people from rural areas, older people, those with disabilities, and people who are involved in low-value conflicts. Nonetheless, the report also critically addresses ongoing obstacles, such as the digital divide, insufficient technology infrastructure, data privacy issues, cybersecurity weaknesses, inadequate stakeholder training, procedural uncertainties, and the reluctance to embrace change among legal professionals. These problems show that ORMs have a lot of potential, but they won't be successful unless they get continued support from policies, investments in infrastructure, and digital literacy across the board. The study also looks at how India's ORM framework may be improved by looking at systems that perform well in the UK, USA, Singapore, and Canada. The investigation indicates that comprehensive laws, uniform procedural regulations, intuitive platforms, and stringent data governance policies are crucial for establishing a dependable and secure ORM ecosystem. The analysis ultimately determines that ORMs are not only technological instruments but transformative entities capable of redefining India's judicial framework. If used wisely and with the right legal changes, ORMs can greatly improve the efficiency of the courts, make it easier for people to get justice, and help the courts reach their long-term goal of building a modern, responsive, and technology-integrated justice system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".