Imagine a courtroom where the intricacies of legal data are handled meticulously, where the scales of justice are balanced with sharp, AI-powered insights. Once a distant dream, the vision is a reality, having made significant inroads in many corners of the globe. Recently, the Punjab and Haryana High Court used ChatGPT to access the worldwide jurisprudence concerning bail when assault involves cruelty. Similarly, American courts employ COMPAS—an AI-powered decision-making software—to assess the potential for recidivism in offenders. These examples demonstrate how global jurisprudence is evolving to embrace AI-driven technologies for justice delivery. AI incorporation can also benefit Pakistan as it grapples with its unique judicial problems.
"Justice delayed is justice denied" is a widely acknowledged sentiment in Pakistan, primarily stemming from the overwhelming pendency of cases within the judicial system. While it's easy to attribute the backlog problem to judges' inability, this would be a distraction. Even if our judges were perfect, expecting an existing number of them to dispose of millions of pending cases is unjustified. From an economic standpoint, Pakistan cannot afford to resolve this issue by simply appointing more judges. The real question is: what else can be done to untangle this Gordian knot?
The solution may be AI fusion into Pakistan's existing judicial system. Today, AI tools can conduct predictive analysis, natural language processing, case law analysis, sentiment analysis, e-discovery, and automated document review. Embracing these techniques can sharply reduce judges' time to review legal files, translate legal documents, search for relevant clauses, and explore case law precedents. This step will allow the judges to channel conserved time to adjudicate extra cases.
Once the public is receptive to AI-assisted court work, its utilisation can be extended in the judicial system. Training algorithms on the provided legal data can be a transformative step to technologise the justice system. Once constitutional law, statutes, ordinances, past judgments, and legal doctrines are fed into an AI learning system, it will continuously optimise its learning algorithm by entering a loop of iterative refinement. Upon attaining a required accuracy level, the system will respond instantaneously to legal questions, thus reducing case backlog by eliminating judicial delays.
The initiative can deliver more than clearing the backlog. In Pakistan, mistrust in the judiciary is an issue stirred by concerns that biases, personal interests, or external pressures may sway judges. AI systems lack emotions, personal biases, or loyalties. AI will generate only data-driven insights, ensuring judicial impartiality. Thus, embracing AI in the judicial system can restore people's faith in the judicial process. Moreover, no one can dictate how the system reaches its conclusions, freeing the judiciary from internal and external pressures.
Integrating AI into the judicial system should not be construed as a replacement for human judges. Instead, it should be viewed as tech support to them, enabling them to deliver justice efficiently and timely
Additionally, the implementation of this AI system will not be a one-man show; feeding legal data and guidelines into the system, supervising its learning, ensuring compliance with standard operating procedures, and directing feedback for continuous improvement will require collaboration among multiple legal experts. This collective effort will embody collective wisdom, representing a significant step forward in judicial reforms.
Integrating AI into the judicial system should not be construed as a replacement for human judges. Instead, it should be viewed as tech support to them, enabling them to deliver justice efficiently and timely. Although the pros of such an initiative outweigh the cons, it is essential to discuss the latter, as unchecked innovation and blind modernisation cannot slaughter accepted standards of the judicial ecosystem, fundamental human rights, and basic tenets of humanity.
First, algorithms learn based on the data provided; if the legal data fed into them is biased, it will translate into algorithmic bias and skewed outcomes. We are living in a multi-ethnonationalist, linguistically diverse, politically polarised, and financially skewed society. Any engineered data against a particular section will arraign the judiciary for manipulating the social matrix. Repercussions will not end here—science will be cursed as a draconian tool for social marginalisation in a society that is already technophobic. Although the consequences are massive, the precaution is simple: avoid data manipulation.
Moreover, transparency demands that such an AI model should be kept under supervised learning rather than unregulated learning. This human-in-the-loop approach is a better solution to the black-box problem associated with AI's opaque decision-making processes.
Furthermore, not all types of cases can be kept under the ambit of AI. For example, cases involving national security should be insulated from the purview of AI to prevent errors that could have far-reaching implications. Another limitation of AI models is that they rely on existing data for learning. The AI may produce divergent results when faced with a case with no jurisprudence or prior legal precedent. Therefore, cases involving novel legal questions should be shielded from the compass of these models.
In a nutshell, AI is a double-edged sword in the realm of justice, carrying benefits and risks. A balanced, cautious approach is essential to harness its potential while preserving judicial integrity.