Summary:
À l’occasion du lancement en 2025 du Cambridge Handbook of Generative AI and the Law, le juge en chef de Nouvelle-Galles du Sud, Andrew Bell, a prononcé des remarques soulignant la nécessité d’une réglementation soigneuse et d’une compréhension de l’IA générative dans les contextes juridiques. L’objectif de ces remarques est de mettre en lumière à la fois les risques potentiels et les avantages de l’IA générative, en particulier en ce qui concerne son utilisation dans le droit et les processus judiciaires. Les points clés incluent l’incidence élevée observée des ‘hallucinations’—sorties fabriquées ou inexactes—par les principaux modèles d’IA dans des contextes juridiques, comme l’a révélé une étude de Stanford de 2024, ainsi que l’importance d’une réglementation bien informée pour équilibrer l’innovation avec la responsabilité et l’exactitude dans le déploiement de l’IA.
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The enthusiasm for the early adoption of generative AI (Gen AI) in an environment with limited regulation has sparked extensive debate among industry leaders, legal experts, and policymakers. As noted by Andrew Bell, the 18th Chief Justice of New South Wales, the rapid uptake of Gen AI technologies is occurring without adequate consideration of the risks, limitations, and ethical concerns associated with these systems. While these technologies promise greater efficiency and access to innovation, their application in critical domains such as law should necessitate a “hasten slowly” approach, drawing inspiration from the Latin maxim festina lente, which advocates for care and deliberation in progress.
From a legal perspective, Gen AI’s deployment raises significant questions regarding accountability and liability, especially in societies where legal systems rely heavily on accuracy and impartiality. Hallucinations—instances where Gen AI systems generate false or misleading output—are one of the most concerning phenomena witnessed. Studies such as the 2024 Stanford investigation cited in the Cambridge Handbook of Generative AI and the Law highlight alarming hallucination rates of 69 to 88 percent for queries involving legal tasks processed by advanced platforms like GPT 3.5, Llama 2, and PaLM 2. In jurisdictions like New South Wales, where judicial integrity is paramount, such error rates underline the pressing need for regulatory frameworks to address quality assurance and error accountability mechanisms. Furthermore, these frameworks should align with existing legal statutes such as the Australian Consumer Law (Schedule 2 of the Competition and Consumer Act 2010 (Cth)), which outlines obligations for manufacturers and suppliers to ensure the accuracy and reliability of products offered for public or professional use.
From an ethical standpoint, the phenomenon of AI hypersuasion—a concept discussed in Chapter 20 of the Cambridge Handbook—poses concerns about exploitation, manipulation, and undue influence. Hypersuasion refers to AI systems’ ability to leverage highly advanced and personalized techniques to influence individual and collective decision-making. In scenarios where legal advice is dispensed by AI systems, the perceived credibility of these systems could lead to users unknowingly accepting inaccurate or biased information. This raises red flags about the erosion of individual autonomy and informed decision-making, principles integral to both ethics and justice.
Industry implications of unchecked Gen AI adoption are far-reaching. For example, in the legal sector, early, unregulated use could lead to materials or arguments based on faulty information, resulting in consequences such as wrongful convictions, loss of public trust, or reputational damage. Beyond law, sectors like healthcare and finance—where precision and accountability are critical—could face similar catastrophes without proper safeguards in place.
To navigate these challenges, regulators and stakeholders must strike an appropriate balance between innovation and risk management. Bell emphasizes the importance of both users and policymakers possessing a comprehensive understanding of Gen AI’s capabilities and limitations to craft meaningful regulations. Legislative models such as the European Union’s AI Act serve as potential templates for integrating accountability, transparency, and human oversight into AI governance structures. Additionally, initiatives for educating users—both professional and lay audiences—about Gen AI’s risks and ethical dilemmas must be prioritized to ensure informed adoption.
Ultimately, while Gen AI offers transformative potential, its implementation must account for legal accountability, ethical soundness, and industry-specific consequences. A “hasten slowly” approach is not merely prudent—it is essential for building systems that are beneficial, equitable, and sustainable for the future.