Introduction
The integration of artificial intelligence (AI) into legal research has transformed how legal professionals access, analyse, and interpret vast volumes of data. AI tools promise efficiency, cost reduction, and enhanced accuracy in tasks such as case law retrieval and document review. However, as highlighted in the statement from R (on the application of Ayinde) v Haringey LBC [2025] EWHC 1383 (Admin) [6], the risks associated with employing AI in legal research are increasingly acknowledged. This essay critically examines these risks, including issues of accuracy, bias, ethical concerns, and over-reliance, with reference to specific examples discussed in academic contexts. It also evaluates whether these risks overshadow the potential benefits of AI in legal research. While acknowledging the transformative potential of AI, this discussion aims to provide a balanced perspective on the challenges that must be addressed to ensure its responsible use in the legal field.
Risk 1: Accuracy and Reliability Concerns
One of the primary risks of using AI in legal research is the potential for inaccuracies in the output generated by these systems. AI tools, often based on machine learning algorithms, rely on the quality and completeness of the data they are trained on. In legal contexts, this can lead to errors if the underlying databases contain outdated or incomplete case law, legislation, or other legal texts. For instance, a widely discussed hypothetical case in academic settings involves an AI tool failing to retrieve a recent precedent that overturns prior rulings, thereby misleading a legal researcher. Such errors can have significant consequences, as legal decisions often hinge on precise and up-to-date information.
Moreover, AI systems may misinterpret ambiguous legal language or fail to account for contextual nuances that human researchers would typically identify. While human oversight can mitigate these risks, the speed and volume of AI-generated outputs may discourage thorough review, increasing the likelihood of undetected mistakes. This concern is particularly relevant in high-stakes litigation, where an inaccurate citation or interpretation could undermine a case. Therefore, despite the efficiency AI offers, the risk of unreliable outputs must be carefully managed.
Risk 2: Bias in AI Algorithms
Another significant risk is the potential for bias embedded within AI systems, which can perpetuate or even exacerbate existing inequalities within the legal system. AI tools are often trained on historical legal data, which may reflect systemic biases in past judicial decisions or societal norms. For example, discussions in law seminars have highlighted cases where AI-driven predictive policing tools, which share similarities with legal research AI, have disproportionately flagged minority communities due to biased training data (Oswald, 2018). Although direct evidence in legal research contexts is less documented, the principle remains: if an AI tool prioritises or omits certain precedents based on biased algorithms, it could skew legal analysis and reinforce discriminatory outcomes.
Addressing this issue requires transparency in how AI models are developed and trained, yet many commercial AI providers treat their algorithms as proprietary, limiting scrutiny. This lack of accountability raises questions about fairness and justice, core principles of the legal profession. Until robust mechanisms are in place to identify and correct bias, the use of AI in legal research carries a tangible risk of undermining equitable outcomes.
Risk 3: Ethical and Professional Responsibility
The ethical implications of AI in legal research further compound the risks. Legal professionals are bound by strict codes of conduct, including duties of competence and due diligence. Relying on AI tools raises questions about accountability: if an AI-generated error leads to professional misconduct or client harm, who bears responsibility—the lawyer, the AI developer, or both? This dilemma was a focal point in class discussions around the hypothetical scenario of a solicitor presenting AI-generated research to a court without adequate verification, only to later discover inaccuracies. Such scenarios highlight the tension between technological reliance and professional obligations.
Additionally, there are concerns about client confidentiality. AI platforms often require data to be uploaded to cloud-based systems for analysis, raising the risk of data breaches or unauthorised access. Given the sensitive nature of legal information, this poses a significant ethical challenge. While encryption and security protocols can reduce these risks, they cannot eliminate them entirely. Thus, ethical considerations must remain at the forefront of any decision to integrate AI into legal practice.
Risk 4: Over-Reliance and Deskilling
A less immediate but equally concerning risk is the potential for over-reliance on AI, leading to the deskilling of legal professionals. If lawyers and researchers become overly dependent on AI tools for routine tasks such as case law searches or contract analysis, critical analytical skills may erode over time. This issue was raised in classroom debates about the long-term impact of AI on legal education, with some arguing that future generations of lawyers might lack the ability to conduct independent research or critically evaluate AI outputs. For instance, an over-reliance on AI summaries could prevent a deeper engagement with primary legal texts, which is essential for developing nuanced legal arguments.
Furthermore, over-reliance may create a false sense of security, where users assume AI outputs are infallible. This complacency can be particularly dangerous in complex cases requiring human judgment, such as interpreting ambiguous statutes or balancing competing legal principles. Therefore, while AI can enhance productivity, it must complement—rather than replace—human expertise.
Do the Risks Outweigh the Benefits?
Despite these risks, the benefits of AI in legal research are undeniable. AI tools can process vast amounts of data at unprecedented speeds, reducing the time and cost associated with manual research. For instance, platforms like ROSS Intelligence have been praised for enabling small law firms to compete with larger counterparts by providing access to sophisticated research capabilities (Ashley, 2017). Additionally, AI can assist in identifying patterns or correlations in case law that might escape human notice, potentially leading to innovative legal arguments.
However, the risks—particularly those related to accuracy, bias, and ethical responsibility—suggest that unchecked reliance on AI could compromise the integrity of legal research. Arguably, these risks do not outweigh the benefits in every context; for routine, low-stakes tasks, AI can be a valuable tool. Yet, in complex or sensitive cases, the potential for error or bias necessitates cautious use and rigorous oversight. Striking a balance between leveraging AI’s advantages and mitigating its dangers is thus essential for its sustainable integration into legal practice.
Conclusion
In conclusion, the risks associated with using AI in legal research, as acknowledged in R (on the application of Ayinde) v Haringey LBC [2025] EWHC 1383 (Admin) [6], are multifaceted and significant. Issues of accuracy, bias, ethical responsibility, and over-reliance highlight the challenges of integrating AI into a field where precision and fairness are paramount. While specific examples discussed in academic settings underscore the real-world implications of these risks, the benefits of AI—, such as efficiency and enhanced data analysis, cannot be dismissed. Ultimately, whether these risks outweigh the benefits depends on context and implementation. For AI to be a net positive in legal research, robust safeguards, transparency, and continuous professional training are essential. Only through such measures can the legal profession harness AI’s potential while preserving the principles of justice and accountability.
References
- Ashley, K.D. (2017) Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press.
- Oswald, M. (2018) Algorithm-assisted decision-making in the public sector: Framing the issues using administrative law rules governing discretionary power. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170359.
- R (on the application of Ayinde) v Haringey LBC [2025] EWHC 1383 (Admin).
