Decoding the Law with AI: A History of Interpretation

by

Author: Denis Avetisyan

This review traces the development of artificial intelligence in legal reasoning, from rule-based systems to the complex capabilities of modern large language models.

A comprehensive analysis of the evolution of AI techniques applied to legal interpretation, encompassing expert systems, argumentation frameworks, and generative AI models.

Despite the inherent ambiguity of legal language, artificial intelligence has increasingly been tasked with formalizing interpretive processes. This paper, ‘Legal interpretation and AI: from expert systems to argumentation and LLMs’, reviews the historical development of this intersection, tracing a path from knowledge-based expert systems and argumentation frameworks to contemporary applications of large language models. We demonstrate how each approach has attempted to capture the nuances of legal reasoning, while also revealing persistent limitations in fully automating interpretive tasks. As generative AI tools become more prevalent in legal practice, can we reconcile their potential for efficiency with the need for transparent, justifiable, and ultimately human-centered legal interpretation?

The Illusion of Legal Meaning

The core of legal reasoning rests on the ability to accurately discern the ‘Ordinary Meaning’ of legal texts – statutes, regulations, and even prior court decisions. This isn’t simply about dictionary definitions, however; it requires understanding how language is actually used by a reasonable person at the time the text was written. Legal professionals must reconstruct the shared understanding of words and phrases, considering the context in which they arose and the common conventions of language. A failure to accurately establish this ‘Ordinary Meaning’ can lead to wildly different interpretations of the same text, undermining the very foundation of a predictable and just legal system. Consequently, significant effort is dedicated to analyzing historical usage, grammatical structure, and the broader linguistic environment to arrive at the most plausible and objective understanding of what the law intends to convey.

Given that language is inherently open to multiple understandings, legal interpretation is susceptible to individual bias and inconsistency. To mitigate this, legal scholars and jurists have developed frameworks like Textualism, which prioritizes the plain and ordinary meaning of legal texts as determined at the time of enactment. This approach aims to constrain judicial discretion by anchoring legal rulings to the expressed words of the law, rather than perceived legislative intent or broader policy considerations. By emphasizing the text itself, Textualism strives to create a more predictable and stable legal system, reducing the potential for judges to impose their own subjective preferences onto the law and ensuring similar cases are decided in a consistent manner. The framework doesn’t eliminate interpretation entirely, but it provides a rigorous methodology for discerning meaning and limiting the scope of reasonable disagreement.

The consistent application of law isn’t simply about understanding words, but also about correctly placing legal concepts into predefined categories – a function served by what are known as constitutive rules. These rules don’t describe how things are, but rather how things become; they define what counts as a particular legal concept, effectively creating the boundaries of a classification. For instance, a rule might define what constitutes ‘due process’ or ‘negligence’, establishing the specific criteria that must be met for something to fall under that legal umbrella. Without these rules, legal interpretation would be adrift in ambiguity, as the same facts could be argued to fit – or not fit – various classifications depending on subjective judgment. Therefore, constitutive rules are foundational to a predictable and equitable legal system, ensuring that legal reasoning isn’t merely a matter of opinion, but a process grounded in established definitions and classifications.

Early AI: A Futile Attempt to Formalize the Informal

Early attempts at applying artificial intelligence to legal reasoning centered on the development of ‘Expert Systems’ in the 1970s and 80s. These systems operated by explicitly representing legal knowledge – statutes, case law, and legal principles – in a formal, machine-readable format. This ‘Knowledge Representation’ involved defining legal concepts and rules using symbolic logic and other formalisms, allowing the system to process information and draw conclusions based on predefined rules. The intention was to replicate the deductive reasoning process of a legal professional, but within a computational framework, and automate tasks like legal diagnosis and advice-giving. Systems like PROLOG were frequently utilized to structure and manipulate this represented knowledge, creating a rule-based system for automated legal reasoning.

Early AI legal systems utilized logical formalisms – specifically, rule-based systems employing techniques like predicate logic and production rules – to represent legal statutes and case law. These systems translated legal concepts into a series of “if-then” statements, allowing for deductive inference to reach conclusions based on provided facts. However, these approaches demonstrated limited scalability and robustness; the rigid structure struggled with ambiguous language, exceptions to rules, and the sheer volume of legal information. The ‘brittleness’ stemmed from the difficulty in comprehensively encoding all possible scenarios and maintaining consistency as the knowledge base expanded, leading to frequent failures when confronted with cases outside the explicitly defined ruleset.

Early attempts at encoding legal knowledge using logical formalisms encountered significant limitations due to the inherent rigidity of the systems. Legal interpretation frequently requires contextual understanding and the ability to handle ambiguity, exceptions, and evolving precedents – capabilities not easily represented by strict rule-based systems. The inability of these systems to gracefully manage the complexities and nuances of legal reasoning-such as considering the intent behind a law or applying it to novel situations-necessitated a shift in research. This prompted exploration of more adaptive methods, including those leveraging statistical learning and, later, machine learning techniques, to better approximate human legal reasoning capabilities.

Machine Learning: Trading Rigidity for Statistical Approximation

Machine learning algorithms are applied to legal interpretation by identifying statistical relationships within large collections of legal documents, including case law, statutes, and regulations. These algorithms, encompassing techniques such as supervised learning, unsupervised learning, and deep learning, can be trained to predict legal outcomes, classify legal issues, or extract relevant information from legal text. The effectiveness of these approaches is directly correlated with the size and quality of the training dataset; larger, well-annotated datasets generally yield more accurate and reliable results. This data-driven approach allows for the automation of tasks previously requiring significant human legal expertise, and facilitates the discovery of patterns and insights that may not be readily apparent through traditional legal research methods.

Large Language Models (LLMs) have become a primary technology in computational legal interpretation due to their capacity to generate human-quality text. These models, powered by Generative AI, are trained on extensive corpora of legal documents – including case law, statutes, and regulations – enabling them to produce novel legal text such as summaries, briefs, and even draft arguments. This generation is achieved through probabilistic modeling of language, where the LLM predicts the most likely sequence of words given an input prompt or context. While not capable of independent legal reasoning, LLMs can effectively synthesize and re-present legal information, assisting legal professionals in research and drafting tasks. The scale of these models, often with billions of parameters, allows for a nuanced understanding and reproduction of legal writing styles and terminology.

Retrieval-Augmented Generation (RAG) is a technique used to improve the performance of Large Language Models (LLMs) in legal interpretation tasks by addressing the limitations of their pre-trained knowledge. RAG functions by first retrieving relevant documents from an external knowledge source – such as legal databases, statutes, or case law – based on a given query. These retrieved documents are then combined with the original query and provided as context to the LLM. This process allows the LLM to generate responses grounded in factual evidence, reducing the likelihood of hallucinations or inaccuracies stemming from its internal parameters and increasing the reliability and verifiability of its outputs. The external knowledge source is not updated during LLM training, allowing for dynamic access to current legal information.

The Illusion of Reasoning: Modeling Argument and Nuance

Contemporary legal reasoning systems are increasingly focused on explicitly modeling the structure of arguments, rather than simply generating legal text. These ‘Argumentation’ systems dissect legal claims into premises, conclusions, and the relationships between them, allowing for a computational representation of how legal professionals build and critique cases. This moves beyond surface-level text analysis to capture the underlying logic – identifying, for example, which claims support others, and where rebuttals or counter-arguments might arise. By formalizing this argumentative structure, these systems can not only evaluate the strength of a legal position but also predict potential challenges and explore alternative interpretations, mirroring the interactive process of legal debate and decision-making.

Legal reasoning frequently encounters situations where general rules are not absolute, necessitating a system capable of handling exceptions and nuanced circumstances. Defeasible reasoning provides this capability, moving beyond simple true/false logic to embrace a more probabilistic approach. Instead of a rule definitively establishing a conclusion, defeasible rules propose conclusions that can be overturned by conflicting information – a ‘defeat’. This allows systems to model the inherent complexities of legal cases, where precedents can be challenged, evidence can be weighed, and arguments can be constructed to demonstrate why a general rule shouldn’t apply in a specific context. By acknowledging that legal conclusions aren’t always certain, but rather depend on the balance of supporting and defeating arguments, these systems achieve a more realistic and robust representation of how legal professionals actually reason.

The pursuit of truly intelligent legal reasoning systems necessitates a move beyond simply processing textual information; integrating advancements in legal semiotics offers a pathway to more sophisticated analysis. Legal semiotics, the study of signs and symbols within the legal domain, provides crucial context often absent from purely computational approaches. By analyzing not just what the law states, but how it is expressed – considering the subtle implications of language, the cultural assumptions embedded within legal texts, and the non-verbal cues present in courtroom interactions – systems can begin to approximate the nuanced judgment of legal professionals. This interdisciplinary approach allows for a deeper understanding of legal arguments, recognizing ambiguities, implicit premises, and the persuasive strategies employed by legal actors, ultimately fostering a more robust and reliable framework for computational legal reasoning.

The trajectory of AI in legal interpretation, as detailed in the paper, feels predictably cyclical. Each generation-from rule-based expert systems to the current fervor around large language models-is hailed as a breakthrough, only to reveal inherent limitations when confronted with the messy realities of production deployments. This mirrors a fundamental truth: any system, no matter how elegantly designed, will eventually succumb to the pressures of real-world usage. As Henri PoincarĂ© observed, “Mathematics is the art of giving reasons.” The paper illustrates this perfectly; each successive AI approach attempts to formalize legal reasoning, yet the very nature of law – its reliance on context, interpretation, and often, ambiguity – resists complete formalization. Documentation, naturally, attempts to capture the ‘reasons’, but it’s a snapshot of a moving target. If a bug in legal AI is reproducible, at least the system is consistently flawed.

What’s Next?

The trajectory outlined within suggests a familiar pattern. Each wave – expert systems, argumentation frameworks, now large language models – arrives heralded as the solution to automating legal reasoning. Each, inevitably, bumps against the irreducible complexity of context, ambiguity, and, frankly, the human capacity for creatively misinterpreting things. It’s a comfort, in a way. If a system crashes consistently, at least it’s predictable.

Future research will undoubtedly focus on mitigating the ‘hallucinations’ and biases inherent in these models. But perhaps a more fruitful avenue lies in accepting those limitations. Rather than striving for artificial judgment, could AI better serve as a sophisticated tool for mapping the range of plausible interpretations? A system that doesn’t claim to know the right answer, but diligently presents all the defensible wrong ones.

One suspects the term ‘cloud-native legal reasoning’ will soon be trending. It will likely mean the same mess, just more expensive. Ultimately, this isn’t about building artificial intelligence; it’s about leaving increasingly detailed notes for digital archaeologists who will one day sift through the wreckage of our algorithms, trying to understand why we thought any of this would work.

Original article: https://arxiv.org/pdf/2603.05392.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-03-06 09:47