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The Ongoing Dilemma: AI in legal Practice

Every day, alarming stories emerge in the legal field. A lawyer utilizes a large language model (LLM) to conduct essential research, integrates that information into a brief, only to discover that the research includes cases that do not exist. When the truth comes to light, the lawyer faces serious repercussions: the judge is outraged, and the disappointed client begins to seek out a more competent attorney.

This recurring scenario has left many bewildered. After all, it is common knowledge that AI systems can produce inaccurate outputs, yet incidents like these continue to unfold. A recent study from Cornell University sheds light on this issue, delving into the problem of overreliance on AI tools and indicating that a wave of serious flaws in AI may be on the horizon. Briefly put, the cost of thoroughly verifying the results produced by these AI systems often outweighs any potential savings gained from their usage. This creates a perplexing paradox.

Understanding Overreliance on AI Tools

In the first part of my analysis on why the AI crisis may be approaching, I examined the dangers associated with an excessive reliance on AI, especially given the deficiencies in the technology’s foundational infrastructure. However, there is more to consider. The fundamental flaws in AI tools concerning reality and transparency pose significant risks. Given the far-reaching implications of these issues, the role of AI in legal practice may end up being more restricted than many optimistically predict.

The Flawed Assumptions Behind AI Adoption

The Cornell study outlines the flawed assumptions driving the rapid integration of AI in the legal realm. A prevalent belief is that AI will ultimately save substantial amounts of time. This time saved is expected to benefit both lawyers and clients, foster fairer billing practices such as alternative fee structures, deliver better outcomes, improve access to justice, and even pave the way for world peace! While the vendors supplying these AI systems may not explicitly claim the last benefit, they often guarantee many others. Meanwhile, commentators portray AI as a transformative force in the legal field, prompting law firms to invest heavily in AI technologies without fully grasping their complexities.

However, these expectations hinge on the unsustainable belief that the time saved will far outweigh the additional steps necessary to validate the AI-generated outputs, mitigating the accuracy issues associated with these tools over time. The Cornell study challenges these expectations, interjecting a dose of reality.

Insights from the Cornell Study

The study identifies two fundamental flaws associated with large language models. The first is widely acknowledged: these systems exhibit a tendency to "hallucinate," generating false or misleading information. Dubbed as a "reality flaw," this issue poses a severe risk in the legal profession, where inaccuracies can have dire consequences. The study also highlights a second flaw related to transparency — the so-called "black box" problem. Essentially, the workings of these AI systems remain opaque, making it challenging to trust their outputs.

As the study articulates, the reality flaw arises from the fact that generative systems are not intrinsically connected to factual accuracy. Instead of learning the underlying facts from the training data, these machine learning models distill vast amounts of data into patterns, which they then reproduce. Notably, the study emphasizes that even legal-specific AI models are susceptible to this flaw.

Consequently, the study concludes that any output generated by AI tools must be verified if the user wishes to ensure its accuracy and relevance to real-world contexts — particularly in the legal domain. In simpler terms, legal professionals must diligently check their citations.

The second flaw pertains to transparency, which presents a trust issue. If users lack insight into the mechanisms of decision-making within these systems, how can they place confidence in their outputs? For a legal ecosystem reliant on sound reasoning and logic, this lack of transparency represents a fundamental challenge. As such, the necessity to understand how a decision was determined is paramount for maintaining the integrity of legal processes and the rule of law.

Alarmingly, the study suggests that neither of these two flaws is likely to be rectified in the foreseeable future.

The Implications of Overreliance

The study elaborates on the broader implications these flaws have for legal practitioners. The myriad cases where lawyers have failed to verify citations and instead relied on "hallucinated" or inaccurate information underscore a troubling trend: many legal professionals underestimate the inherent risks of using AI. Alternatively, they may have been misguided by service providers into believing that such risks are minimal.

These practitioners have excessively relied on technology that they thought was more reliable than it actually is. The fallout has been a collective outcry about the necessity of checking citations. This warning is often accompanied by a knowing smirk, implying that the blame lies solely with negligent or lazy attorneys. However, the problem is far more systemic and complex, as the study suggests that the net value of an AI model in legal practice can only be accurately assessed when the efficiency gains — time saved, salary reductions, and resource conservation — are weighed against the costs incurred for verifying AI-generated outputs.

The Verification Paradox Explained

Given the high stakes associated with accuracy in legal work, the study reveals that verification costs in many legal actions are frequently prohibitively high, thereby undermining the savings expected from using AI. Moreover, this verification cost is not alleviated by the use of automated systems, as the existing risks associated with reality and transparency remain. This situation leads to what the study terms the "verification paradox."

The repercussions of this paradox have already become evident. Courts have begun imposing penalties due to lawyers presenting erroneous cases, and there will likely be an uptick in malpractice claims as well as ethical violations. The cost of inaccuracy within the legal domain is far too significant to forgo thorough verification.

That said, while AI has shown remarkable capabilities in various contexts where the risks of error are minimal, its utility in legal environments remains questionable. As the study asserts, "the more important the output, the more critical it is to verify its accuracy."

Conclusion: A Call for Caution

The Cornell study concludes with a sobering reality: the "verification-value paradox" indicates that the net value of AI within the legal profession may be significantly inflated due to an underestimation of verification costs. A comprehensive understanding of the costly yet essential nature of validation leads to the conclusion that AI’s net value will often be negligible in legal practice. In most situations, the benefits conferred by AI do not justify the accompanying verification expenses.

This economic reality becomes starkly apparent when comparing the costs of outsourcing a piece of work to an LLM against utilizing a human professional. For instance, if you rely on an LLM to conduct legal research that typically requires ten hours of manual input, you’re likely to receive results that include numerous case citations. Subsequently, you must verify each citation’s existence and ensure that it supports the claims made by the LLM. By the time you substantiated those citations, it may take you eight hours or more, effectively negating any time that may have initially been saved.

Preparing for a Possible Eruption

While it may be too late to entirely retract AI’s integration into legal practice, the implications are clear. If it takes just as long, if not longer, to validate the outputs of an AI tool that your firm has invested heavily in, you’ll likely be less inclined to invest further. Clients may also express dissatisfaction when the use of AI doesn’t reduce costs but instead increases them while exposing them to risk.

The prevailing sentiment may shift towards the conclusion that the risks and costs associated with AI utilization are simply too high to justify its continued use, undermining enthusiasm and trust in the technology. As lawyers realize that the costs may not be worth the potential advantages, many may find themselves with expensive systems that fail to deliver value. Vendors may need to reevaluate their offerings, and venture capital could face significant losses. The figurative volcano of discontent and skepticism is poised to erupt.

As legal professionals navigate this landscape, the emphasis must remain on meticulous verification. In this regard, AI legalese decoder can provide essential assistance. By streamlining the process of interpreting legal jargon and decoding complex language, it helps attorneys evaluate the outputs generated by AI quickly and effectively. This tool not only aids in identifying discrepancies but also serves as a critical support mechanism for maintaining the integrity that the legal profession demands.

In the interim, the call to action is clear: meticulously check your citations and validate your sources before placing trust in AI-generated outputs. This diligence is not merely a suggestion; it is a necessary practice in a field where the implications of errors are profound.

Stephen Embry is a lawyer, speaker, blogger, and writer who publishes TechLaw Crossroads — a blog devoted to examining the intersections of technology, law, and legal practice.

Melissa Rogozinski is the CEO of the RPC Round Table and RPC Strategies, LLC, a marketing and advertising firm in Miami, FL.

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