The integration of large language models (LLMs) such as ChatGPT into the legal field has ignited a transformative dialogue about the future of legal analysis, writing, and education. The promise of harnessing artificial intelligence to streamline legal workflows and augment human expertise is tantalizing, yet significant limitations and challenges remain that temper enthusiasm with caution. A new comprehensive survey published in Humanities and Social Sciences Communications delves deeply into the multifaceted implications of deploying LLMs in legal systems, offering a critical lens on their capabilities, risks, and ethical considerations.
At the heart of the discussion is the fundamental design of ChatGPT and similar LLMs as general-purpose language models. Unlike specialized AI tailored for medical diagnostics or financial forecasting, these models have not been explicitly trained on the nuanced complexities of legal analysis or the intricacies involved in constructing robust test solutions for academic or professional use. This generic training results in inconsistent performance, especially in high-stakes environments such as law school examinations where precision and rigor are paramount. The models’ occasional lapses in accuracy prevent them from being considered reliable substitutes for seasoned legal practitioners when interpreting, drafting, or advising on legal matters.
Legal educators are particularly concerned about the ethical and academic integrity issues arising from the advent of AI tools in the classroom and examination settings. The risk that students might leverage AI models to generate responses without sufficiently understanding the material challenges educational frameworks and assessment standards. This creates a pressing need for revised policies and oversight mechanisms to ensure that AI’s integration enhances learning rather than undermining the development of critical legal reasoning skills. As the survey highlights, educators must strike a delicate balance between embracing innovation and preserving the integrity of legal training.
Beyond academia, the deployment of LLMs in practical legal scenarios introduces a new dimension of ethical and professional dilemmas. One chief concern is the potential dissemination of inaccurate or misleading legal information. ChatGPT’s outputs may not always align with the latest legal precedents or the specificities of jurisdictional requirements, which creates an inherent risk in client advisory contexts. This problem is compounded by the model’s inability to fully understand confidential client information or adhere consistently to the rules of professional conduct that govern attorney behavior. Consequently, deploying LLMs without rigorous human oversight could inadvertently compromise client confidentiality or breach ethical obligations.
The dynamic and evolving nature of law further accentuates the limitations of LLMs. Legal statutes, regulations, and case law are in a constant state of flux, driven by legislative changes and pivotal court rulings. Since the knowledge base of models like ChatGPT is fixed at the point of their training cutoff and is not updated in real-time, their legal knowledge can become obsolete rapidly. This temporal lag underscores the irreplaceable value of trained human lawyers who continually monitor recent developments and integrate them into their legal reasoning, ensuring that advice remains current, legally sound, and tailored to individual cases.
Another critical aspect examined in the survey is the ethical considerations surrounding intellectual property laws and confidentiality when using generative AI tools. Legal practitioners must ensure that the AI-generated content complies with professional standards, respects copyright and data protection laws, and safeguards client privacy. The opaque nature of AI training datasets, which might incorporate proprietary or sensitive information, raises significant concerns about the provenance and ownership of AI-produced legal texts. Ensuring accountability and transparency in how these models are trained and deployed is vital for maintaining trust within the legal community and with the public.
Bias and inaccuracies embedded within AI responses present yet another layer of complexity. Generative AI systems are shaped by the vast corpora on which they are trained, reflecting existing societal biases, systemic inequalities, and incomplete representations of legal realities. If unchecked, these biases can propagate unfair or discriminatory legal interpretations, jeopardizing justice and equity. Legal professionals must therefore exercise due diligence in vetting AI outputs, employing AI as an assistive tool rather than an unquestionable authority, and remaining vigilant against the inadvertent amplification of biased viewpoints.
Transparency and accountability remain outstanding challenges in the adoption of LLMs in law. Unlike human lawyers who are required to pass rigorous qualification exams and operate under strict ethical codes, LLMs have no formal certifications or professional accountability mechanisms. Their inner workings—the so-called “black box” nature of deep learning models—obfuscate how specific outputs are generated, making it difficult to trace or justify legal advice retrospectively. This opacity demands continuous human oversight to validate the correctness and ethical soundness of AI-generated legal information, reinforcing that LLMs should augment rather than replace human legal expertise.
Despite these myriad concerns, the authors of the survey acknowledge the significant promise that LLMs hold for various legal applications. From automating routine document drafting to facilitating access to legal information for underserved populations, these technologies have the potential to democratize legal services and increase efficiency. However, realizing these benefits requires that the legal community actively address the pitfalls identified, developing frameworks for ethical AI usage and ensuring that technology serves as a complement, not a crutch.
The survey also contemplates broader methodological limitations in studying the role of LLMs in law. Given the rapid advances in natural language processing, emergent model architectures and datasets may quickly render current evaluations incomplete. Moreover, the bulk of research to date predominates in English-language jurisdictions, limiting insights into how multilingual or diverse legal systems interface with AI technology. Proprietary models and unpublished approaches restrict the transparency of progress, potentially censoring critical evaluation and comparison.
Importantly, the overview presented in this survey is based on existing literature and reported findings rather than primary experimental evaluations or benchmarking. As a result, conclusions are contingent upon the quality, scope, and inherent biases of the prior studies they synthesize. The absence of long-term deployment data leaves open questions about the real-world impact, scalability, sustainability, and evolving risks of integrating LLMs into legal ecosystems.
In conclusion, while the advent of large language models represents a watershed moment for the legal domain, careful deliberation is paramount. The integration of AI tools like ChatGPT into legal writing, education, and practice demands a nuanced understanding of their current capabilities and inescapable limitations. Legal professionals, educators, and technologists must collaborate to develop adaptive, ethical, and transparent usage guidelines. Only through ongoing vigilance and human stewardship can AI’s promise be harnessed without compromising the principles of justice and professionalism that underpin the law.
This survey serves as a crucial wake-up call to the legal community—prompting reflection, innovation, and responsible adoption of AI technologies. By embracing these challenges head-on, the age-old pursuit of justice can be advanced in tandem with cutting-edge technology, steering the future of legal systems toward a more equitable and efficient horizon.
Subject of Research:
Large Language Models and their application, limitations, and ethical implications in legal systems.
Article Title:
Large Language Models in Legal Systems: A Survey
Article References:
Dehghani, F., Dehghani, R., Naderzadeh Ardebili, Y. et al. Large Language Models in Legal Systems: A Survey. Humanit Soc Sci Commun 12, 1977 (2025). https://doi.org/10.1057/s41599-025-05924-3
Image Credits:
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