Large Language Models, Artificial Intelligence and the Future of Law

LLMs can replace some judicial functions due to three reasons:

• Efficiency: LLMs can analyze vast amounts of data and legal documents much faster than humans. This can lead to quicker resolutions of cases, which can be particularly valuable in systems burdened by backlogs and slow processing times.

• Neutrality: LLMs can potentially offer an unbiased approach when handling judicial tasks. Since they do not possess personal beliefs, experiences, or prejudices, their analyses and decisions are based solely on the data and legal precedents they have been trained on. This could help in minimizing subjective interpretations and personal biases that might influence human judges. 

• Predictability: LLMs can process information and apply legal rules with a high degree of consistency. By consistently applying legal rules and precedents to similar situations, LLMs can help create a more predictable legal environment. It also aids in upholding the rule of law, where the law is applied consistently and predictably, irrespective of individual circumstances.

AI is already being used for several courtroom tasks

• USA: TAR (document review), COMPAS (risk assessment), PATTERN (risk assessment)

• Canada: British Columbia’s Civil Resolution Tribunal (Small Claims Online Dispute Resolution)

• UK: MoneyClaims Online (Online Dispute Resolution), English Traffic Penalty Tribunal (Online Decision Making)

• China: Hua Yu (Pre-trial Document Analysis), Xiao Zhi (Trial Assistance), Smart Judge (Judgement Assistance), Justice Flag ( Predictive Alert)

• India:  SUPACE (Supreme Court Portal for Assistance in Court Efficiency)

  • Judgments on the use of AI:

    • Jaswinder Singh v. State of Punjab (PHHC) 

    • Christian Louboutin SAS & Anr. v. M/s The Shoe Boutique (DHC)

    • Punjab State Electricity Board vs Pushwinder Kaur And Ors (PHHC)

    • Ms Asus India P Ltd vs Cc Air Cargo Ch (DHC)

There is still scepticism among the judiciary

Risks and Challenges

1. Lack of Humanness: LLMs may struggle to grasp the nuances of human emotions, ethical considerations, and the broader social context of legal cases. Legal decision-making often involves interpreting laws within the complex fabric of societal values and individual circumstances, something that machines are currently ill-equipped to handle with the depth and empathy that a human judge might offer.

2. Algorithmic Bias: Despite their potential for neutrality, LLMs can inadvertently perpetuate or even amplify biases present in their training data. If the historical data or past legal decisions used to train an LLM contain biases, the model could continue to reflect or enforce these biases, potentially leading to unfair or discriminatory outcomes.

3. Accountability and Transparency: Decisions made by LLMs can be difficult to interpret or challenge, as the reasoning processes of machine learning models are often opaque. This lack of transparency can complicate accountability, especially in a legal context where the rationale behind a decision is as important as the decision itself.

4. Cyber Security and Privacy: Using LLMs in the judiciary involves handling sensitive personal and legal data. Ensuring the security and privacy of this data is crucial. Given that LLMs operate based on data inputs, there's a risk of manipulation or hacking where the inputs are tampered with to influence the model's outputs. Such vulnerabilities could be exploited to alter legal outcomes.

5. Judicial Legitimacy: The public's trust in the judicial system is partly rooted in the human elements of justice—empathy, moral reasoning, and accountability. Decisions rendered by machines may lack the perceived legitimacy of those made by human judges, especially in complex or sensitive cases. If stakeholders believe that decisions are being made by algorithms rather than human judges, it could erode trust and acceptance of the judiciary, impacting the overall effectiveness of the legal system. (See for eg. Starke et. al. 2020)