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Principles & practices for AI use in courts

A guide for responsible use of generative AI

Generative AI is reshaping the justice system, offering tools for text generation, document analysis, and task automation. This guide from the Thomson Reuters Institute/NCSC AI Policy Consortium for Law and Courts outlines ethical principles for responsible AI use, emphasizing the need for judges, court administrators, and legal professionals to use technology correctly.

Who should read this?

  • Judges & court administrators: Understand AI implementation and learn more about ethical considerations and potential risks
  • IT leaders & policymakers: Develop AI standards, ethical frameworks, and risk mitigation strategies
  • Court staff & legal professionals: Learn about AI's potential benefits and how your court can use it ethically and responsibly

Why this guide matters

Generative AI (GenAI) is transforming how courts work, but it is a tool that must be used with care. A clear understanding of the risks, limitations, and ethical best practices is vital for unlocking the vast potential of AI in modernizing your court.

Download the guide

Strategies for implementing AI

Start small

Begin with a measured approach that mitigates risk to core court functions, and then gradually expand AI use with regular evaluations.

Set clear policies

Your court should have written policies that define acceptable uses for AI and how to respond if things go wrong.

Find the why

Ensure that AI is used properly to solve problems, and make sure that all risks have been considered and accounted for.

Conduct regular reviews

The rapid evolution of AI demands regular checkups to make sure AI aligns with your court's values and objectives.

Key foundations for the ethical use of AI

The benefits of integrating AI into your court's daily operations come with the responsibility of ensuring the technology is used ethically. The level of human oversight required depends on the specific use, with minimal risk use requiring a "human-on-the-loop" to monitor processes and outcomes. High-risk AI use requires a "human-in-the-loop" to be actively involved in training and guidance and provide direct oversight and intervention when needed.

Use as a valuable assistant

AI can be a valuable assistant, but it should never replace human judgment and should be used with human supervision.

Review for accuracy

All AI-generated content should be reviewed for accuracy.

Safeguard sensitive data

Your AI tools must safeguard sensitive data, comply with security protocols, and never compromise confidentiality.

Be transparent

Your use of AI should be transparent to the public, and clear records should be kept.

Conduct regular evaluations

Regular evaluations are vital to detect the potential for bias in your AI, especially when it comes to high-stakes legal decisions.

Ongoing education

All court staff need ongoing education to keep up with changes in AI capabilities and risks to ensure ethical use.

Prevent plagiarism

Any content generated by AI should be reviewed to prevent unintentional plagiarism.

Balancing the risk of AI in your court

Your court's AI use should consider four key risk areas, each of which demands a different level of human involvement.

Minimal risk

This covers routine AI tools such as those used for word processing and needs supervisory oversight from a human-on-the-loop.

Moderate risk

Tasks such as having AI draft opinions or conduct research requires verification and quality checks from a human-in-the-loop.

High risk

Any AI output affecting legal rights, such as risk predictions, demands significant review and decision-making from a human-in-the-loop.

Unacceptable risk

AI should never be used to automate decisions on life, incarceration, family, or housing matters.

Understanding common risk areas

Our experts have identified the three top risk areas that your court should focus on when using AI. Read our complete set of principles to discover the risks.

TRI/NCSC AI Policy Consortium

An intensive examination of the impact of technologies such as generative AI (GenAI), large language models, and other emerging, and yet-to-be developed tools.