On April 29, 2026, Pakistan's National Judicial (Policy Making) Committee released the National Guidelines for the Use of Artificial Intelligence in Judicial Institutions, approved at the Committee's 57th meeting. Developed by the National Judicial Automation Committee under Supreme Court Justice Muhammad Ali Mazhar, the framework does something unusually disciplined for a national AI policy: it tells courts exactly where machines may help and where they may not. AI is confined to case management, legal research, predictive analytics and document processing. Judges, the guidelines insist, remain the "ultimate arbiters" (Dawn).
That boundary deserves credit. It is the rare government document that resists the temptation to either ban the technology or hand it the keys.
The problem the guidelines are actually trying to solve
Start with the strongest case for caution — and for action. Pakistan's courts are buried. According to data compiled by the Law and Justice Commission, roughly 2.36 million cases were pending as of December 31, 2024, with about 83 percent stuck in the district judiciary (Minute Mirror). Peer-reviewed analysis of the same trend confirms a backlog hovering near two million for years, corroding the constitutional promise of speedy justice (Pakistan Social Sciences Review).
A litigant who waits seven years for a tenancy dispute to resolve has effectively been denied a remedy. That is not an abstraction in Pakistan; it is the median experience. So the instinct to reach for any tool that clears the docket faster — including AI triage, automated cause-list management and instant precedent retrieval — is entirely rational. The skeptics who worry that rushed automation could entrench bias in bail and sentencing, or that opaque models could quietly displace reasoned judgment, are also right to worry. Both pressures are real, and they pull in opposite directions.
What the guidelines do — and don't — permit
The NJPMC framework threads that tension by drawing a functional line rather than a blanket one. The four permitted uses are all upstream of the decision: organising files, surfacing relevant law, processing documents, flagging patterns. The guidelines pair this with safeguards against bias, an explicit demand for explainability and accountability, and strict privacy and data-security standards for litigants (The News). Crucially, they preserve the administrative autonomy of each high court to adopt at its own pace, which avoids forcing a uniform rollout onto provinces with wildly different capacity.
This is proportionate regulation in the precise sense the term should carry. It does not legislate against a hypothetical robot judge — a fear that makes for good headlines but describes no product any court is procuring. It regulates the actual risk surface: a research tool that fabricates a citation, a triage model trained on historically skewed data, a vendor that harvests litigant records. Those are tractable, auditable failure modes, and naming them is the first step to managing them.
Built on a ruling, not a press release
The guidelines did not appear from nowhere. They operationalise the Supreme Court's 2025 judgment in Ishfaq Ahmed v. Mushtaq Ahmed (PLD 2025 SC 582), authored by Justice Syed Mansoor Ali Shah, which arose from exactly the kind of seven-year tenancy dispute described above. The Court grounded the right to expeditious justice in Articles 10A and 37(d) of the Constitution and held that AI should be "welcomed with careful optimism" — useful for research, drafting clarity and case-flow analytics, but never permitted to usurp the judge's interpretive role (Sociology & Cultural Research Review). The judgment was unusually clear-eyed about the downside, cataloguing hallucinated citations, black-box opacity and the risk of perpetuating bias.
That lineage matters. A guideline derived from a reasoned constitutional ruling carries more legitimacy — and more durability — than one improvised by a ministry. It also means the policy and the case law point the same direction, reducing the odds that early adopters get whipsawed by litigation.
The real test is capacity, not caution
Here is where our optimism turns practical. Pakistan is not starting cold: the Federal Judicial Academy's "Judge-GPT" research assistant has reportedly been adopted by around 1,500 district judges (SLD). Demand from the bench clearly exists. The guidelines' value is that they give those judges a sanctioned lane rather than leaving them to experiment with consumer chatbots in legal gray zones.
But a framework is not a fix. The backlog's documented causes — judicial vacancies, frequent transfers, weak infrastructure — are not problems an inference engine resolves. AI can cut the minutes a judge spends finding a precedent; it cannot fill an empty bench. The danger is not that these guidelines are too permissive but that they become a substitute for the harder, unglamorous work of hiring judges and digitising records. If the framework's explainability and audit requirements are enforced only on paper, Pakistan gets the risks of automation without the accountability.
The right posture, then, is to ship the tools, log every model decision, publish the audit trails, and measure disposal rates against a hard baseline. Confine AI to the back office, keep judgment human, and treat the guidelines as a floor for experimentation rather than a ceiling. On that test, Pakistan has written a model worth copying — provided it now does the boring part.