Courtroom AI: Artificial Intelligence or Anti-Intelligence?

Not very long ago, there was no technological support to assist courtroom procedure. Courtroom practice relied almost entirely on human ingenuity, with precedents serving as valuable guiding forces, allowing man’s thinking to flourish and expand. This very innate human capacity for innovative problem-solving led to the integration of AI in the courtroom to supplement, rather than substitute, legal intellect and judicial reasoning.

The idea was simple: make research easier, draft arguments better, deliver quick justice. Over the years, however, we partook in widespread oversight.

Promising Tools
If only the results were so straightforward. Before hopping onto the operational realities faced by aggrieved parties, the normative constructs need to be studied first. Some ideals, indeed, work not just on paper but also in practice. Large language models have augmented the approach to justice dispensation in fundamental ways, streamlining complicated processes and automating multi-step workflows.

Evidentiary examples aren’t so far-fetched. While stenographers once played a pivotal role in the progression of judicial proceedings, over 4,000 courts across the country now avail of AI transcription services such as Adalat AI, which transcribe spoken words in real-time, easing the burden on human effort. With AI, the focus of justice centres on vital substantive issues rather than procedural formalities. Whether this proves an antidote to outdated legacy remains to be seen, but the early signs are encouraging.

Generative AI plays a transformative role in translating written submissions into audio formats in local languages and dialects, aiding the illiterate, the impoverished, and non-English speakers. By tackling the language barrier, AI conveys justice as something meaningful rather than merely lip service. Judicial officers have relied on chatbots like ChatGPT to explain their reasoning to the common man. With lauders, of course, have come skeptics. The Chief Justice of India has advised caution when utilising AI, the Gujarat High Court has implemented a strict no-AI-use policy, and the Punjab and Haryana High Court has issued a similar advisory. The principle, in essence, holds that AI is ancillary to human intelligence, not the other way around, particularly where parties’ lives are at stake.

From the standpoint of case management, the “e-Courts” initiative has consolidated the nation’s cases in accessible, digitised format. The precise extent to which the project is clearing the backlog is unclear, though success in certain domains deserves acknowledgement: augmented real-time digital services have eased communication between courts, litigants and advocates; several virtual courts have been established; and courthouses are slowly becoming paperless. With slow yet sustained effort, the backlog can be meaningfully reduced.

AI’s execution also extends beyond the courtroom, into executive functions. Law enforcement institutions have deployed predictive analysis tools; the police force has AI employed AI for facial recognition, suspect tracking, and behavioural analysis through repetitive patterns. These initiatives mirror algorithmic risk assessment metrics used in the United States to reduce recidivism. Though penologically and criminologically fruitful, critiques exist regarding their violative nature and accuracy amongst algorithmic biases.

Familiar Dangers
These are unprecedented developments in the techno-legal sphere that undoubtedly represent a watershed moment in the delivery of justice. For legal professionals, AI tools make the research, analysis, and drafting process more efficient, allowing them to focus on substantive issues rather than presentational fineries. Economically, growing aggregate demand has prompted several technology companies to introduce comprehensive product suites, helping lawyers and law firms capitalise on their competitive advantage.

Yet with such massive leverage come real pitfalls. Recent irregularities involving GenAI-drafted documents have generated fictitious case law, producing hallucinations and consequent judicial condemnation. The Bombay High Court imposed costs for “dumping” unverified legal material; the Gujarat High Court expressed concern over fake precedents cited in a GST case.

These cases reflect not only the epistemic uncertainty inherent in AI-generated outputs but are amount to what the Supreme Court has described as “misconduct”.

The advantageous nature of AI in the courtroom is a double-edged sword. Stenographers do not merely type down spoken words; they comprehensively grasp the nuance and context of parties and cases. The extent to which AI software can contextually understand human emotions such as anger and sarcasm, figurative sentences, and monosyllabic answers during interrogation remains unestablished. In translation, research has shown that “gendered AI” reflects prevailing stereotypes, further undermining the credibility of parties and witnesses.

So, if the efficacy of AI in the courtroom seems to be such a misnomer, what next?

There is, as yet, no perfect substitute for human judgement. A a selective approach is therefore necessary when leveraging AI to tackle judicial backlogs. A balanced model of human-first thinking with subsequent AI-powered polishing is ideal, though arriving at this equilibrium is a Herculean task that will take years.

Justifications generated by LLMs cannot replace judicial intellect, which involves legal reasoning, critical thinking, and above all contextual understanding of both the law and the humans involved. Even from a commercial standpoint, undisclosed algorithms remain the black box of the twenty-first century, and smokescreens cannot be cleaned overnight, though eventually they need to be.

Young Professionals
AI is a necessary tool to learn, a necessary skill to imbibe, and a necessary ice-breaker in any professional discussion today. But necessity shouldn’t mean neglect. Earnest efforts must be made to prevent it from becoming an anathema to effective justice, lest an equitable society becomes a utopian construct. In courtrooms, in law firms, in individual practice, and in law schools, AI training can be directed at low-hanging fruit rather than more complicated and nuanced matters.

From law school onwards, fresh professionals must be taught the importance of the three Cs: content, context and conviction. One without the other two causes a power imbalance, rendering the ideal of justice meaningless. This integration teaches a valuable lesson: convenience without conviction is not merely a judicial procrastinator. It could mean the difference between life and death.