Yonathan A. Arbel, Thinking the Unthinkable: AI in the Service of Justice, Capstone Lawyer (2024).

unpaginated Capstone Lawyer online essay

Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Opening Illustration,” that a Fairhope trampoline installation dispute raised the ordinary-language question whether that work counted as landscaping under an insurance policy. The discussion situates this proposition within the opening illustration of the Snell trampoline-insurance dispute. This is significant because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language. It connects to Snell v. United Specialty Insurance, insurance interpretation, ordinary meaning, trampoline installation, landscaping, AI-assisted judging.

a Fairhope trampoline installation dispute raised the ordinary-language question whether that work counted as landscaping under an insurance policy. This matters because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language.

Snell v. United Specialty Insurance
insurance interpretation
ordinary meaning
trampoline installation
landscaping
AI-assisted judging

The dispute is an illustrative entry point; the essay does not claim that the LLM responses controlled the coverage judgment.

The online essay's “Opening Illustration” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Opening Illustration,” that the insurer's denial of coverage transformed a commonplace word into a consequential legal-interpretation problem. The discussion situates this proposition within the opening illustration of the Snell trampoline-insurance dispute. This is significant because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language. It connects to Snell v. United Specialty Insurance, insurance interpretation, ordinary meaning, trampoline installation, landscaping, AI-assisted judging.

the insurer's denial of coverage transformed a commonplace word into a consequential legal-interpretation problem. This matters because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Opening Illustration,” that the Snell dispute illustrates how artificial intelligence may assist courts in pursuing justice through better language interpretation. The discussion situates this proposition within the opening illustration of the Snell trampoline-insurance dispute. This is significant because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language. It connects to Snell v. United Specialty Insurance, insurance interpretation, ordinary meaning, trampoline installation, landscaping, AI-assisted judging.

the Snell dispute illustrates how artificial intelligence may assist courts in pursuing justice through better language interpretation. This matters because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Opening Illustration,” that a factually modest case can become a catalyst for a broad change in how law understands and applies words. The discussion situates this proposition within the opening illustration of the Snell trampoline-insurance dispute. This is significant because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language. It connects to Snell v. United Specialty Insurance, insurance interpretation, ordinary meaning, trampoline installation, landscaping, AI-assisted judging.

a factually modest case can become a catalyst for a broad change in how law understands and applies words. This matters because it shows how an ordinary coverage dispute can expose foundational uncertainty about legal language.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that courts traditionally interpret legal language with dictionaries, precedent, Latin canons, and judges' linguistic intuition. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

courts traditionally interpret legal language with dictionaries, precedent, Latin canons, and judges' linguistic intuition. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.

dictionaries
precedent
linguistic canons
judicial intuition
context
ordinary meaning

The essay identifies limitations in traditional tools but does not contend that dictionaries, precedent, canons, or judicial judgment should be abandoned.

The online essay's “The Challenge of Legal Interpretation” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that dictionaries can be outdated and insensitive to the context that gives words their practical meaning. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

dictionaries can be outdated and insensitive to the context that gives words their practical meaning. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that precedent promotes consistency but can lag behind changes in contemporary language usage. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

precedent promotes consistency but can lag behind changes in contemporary language usage. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that judicial expertise does not guarantee familiarity with modern or vernacular speech. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

judicial expertise does not guarantee familiarity with modern or vernacular speech. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that mistaken interpretation can produce unjust results, prolonged litigation, and inconsistent law. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

mistaken interpretation can produce unjust results, prolonged litigation, and inconsistent law. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “The Challenge of Legal Interpretation,” that legal practice has long needed a more robust and context-aware source of evidence about meaning. The discussion situates this proposition within the essay's critique of conventional tools for resolving ordinary meaning. This is significant because it identifies the interpretive shortcomings that generative interpretation is designed to supplement. It connects to dictionaries, precedent, linguistic canons, judicial intuition, context, ordinary meaning.

legal practice has long needed a more robust and context-aware source of evidence about meaning. This matters because it identifies the interpretive shortcomings that generative interpretation is designed to supplement.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that the trampoline case marked the first judicial use of generative interpretation as an interpretive approach. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

the trampoline case marked the first judicial use of generative interpretation as an interpretive approach. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.

generative interpretation
large language models
training data
statistical language knowledge
contextual meaning
David Hoffman

This magazine essay summarizes the method; its claims about language knowledge and interpretive value should be read with the fuller safeguards in the underlying scholarship.

The online essay's “Enter Generative Interpretation” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that large language models can offer evidence about how language is used and understood in real-world contexts. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

large language models can offer evidence about how language is used and understood in real-world contexts. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that training on text at a scale beyond any individual reader gives LLMs unusually broad statistical language knowledge. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

training on text at a scale beyond any individual reader gives LLMs unusually broad statistical language knowledge. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that LLMs can generate context-aware accounts of ordinary meaning across different domains and communities. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

LLMs can generate context-aware accounts of ordinary meaning across different domains and communities. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that the method aims to draw on an LLM's language knowledge rather than treat its output as unexplained authority. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

the method aims to draw on an LLM's language knowledge rather than treat its output as unexplained authority. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Enter Generative Interpretation,” that generative interpretation builds on Arbel's joint scholarship with Professor David Hoffman. The discussion situates this proposition within the account of why large language models may illuminate real-world language usage. This is significant because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning. It connects to generative interpretation, large language models, training data, statistical language knowledge, contextual meaning, David Hoffman.

generative interpretation builds on Arbel's joint scholarship with Professor David Hoffman. This matters because it states the mechanism by which an LLM could contribute evidence about contemporary ordinary meaning.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that Judge Kevin Newsom openly described the use of AI language models in interpretation as a possible heresy. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

Judge Kevin Newsom openly described the use of AI language models in interpretation as a possible heresy. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.

Judge Kevin Newsom
Eleventh Circuit
judicial concurrence
ChatGPT
ordinary meaning
Snell

Judge Newsom wrote a concurrence, and the model answers were not determinative of the case; the episode does not establish binding acceptance of the method.

The online essay's “Judicial Recognition and Future Prospects” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that Newsom's concurrence relied extensively on Arbel and Hoffman's work when assessing LLMs as evidence of ordinary meaning. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

Newsom's concurrence relied extensively on Arbel and Hoffman's work when assessing LLMs as evidence of ordinary meaning. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that Newsom asked multiple models whether installing an in-ground trampoline is landscaping. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

Newsom asked multiple models whether installing an in-ground trampoline is landscaping. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that the models agreed that an in-ground trampoline installation would ordinarily count as part of a landscaping job in the supplied context. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

the models agreed that an in-ground trampoline installation would ordinarily count as part of a landscaping job in the supplied context. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that the models' affirmative answers were informative but not determinative because other legal factors governed the dispute. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

the models' affirmative answers were informative but not determinative because other legal factors governed the dispute. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that the concurrence made it plausible within mainstream judging that ChatGPT might say something useful about everyday legal language. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

the concurrence made it plausible within mainstream judging that ChatGPT might say something useful about everyday legal language. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Judicial Recognition and Future Prospects,” that judicial engagement with LLMs marks a milestone without yet amounting to wholesale acceptance of AI-assisted interpretation. The discussion situates this proposition within the discussion of Judge Kevin Newsom's concurrence in the Eleventh Circuit. This is significant because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision. It connects to Judge Kevin Newsom, Eleventh Circuit, judicial concurrence, ChatGPT, ordinary meaning, Snell.

judicial engagement with LLMs marks a milestone without yet amounting to wholesale acceptance of AI-assisted interpretation. This matters because it documents an early judicial encounter with generative interpretation while preserving the distinction between useful evidence and legal decision.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that generative interpretation requires practical safeguards against prompt bias, hallucination, and uncritical reliance. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

generative interpretation requires practical safeguards against prompt bias, hallucination, and uncritical reliance. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.

cross-model verification
prompt documentation
replicability
human oversight
hallucination
advisory evidence

The examples are demonstrations and recommendations rather than controlled evidence that LLM assistance improves outcomes in every interpretive dispute.

The online essay's “Practical Applications and Best Practices” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that Hurricane Katrina insurance cases illustrate how LLMs might clarify the ordinary relation between flood and storm surge. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

Hurricane Katrina insurance cases illustrate how LLMs might clarify the ordinary relation between flood and storm surge. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that prenuptial-agreement disputes illustrate how LLMs might illuminate context-dependent ambiguous terms. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

prenuptial-agreement disputes illustrate how LLMs might illuminate context-dependent ambiguous terms. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that commercial-contract disputes illustrate how domain-sensitive LLM evidence might help generalist judges understand industry jargon. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

commercial-contract disputes illustrate how domain-sensitive LLM evidence might help generalist judges understand industry jargon. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that generative interpretation is meant to complement dictionaries, precedent, and human judgment rather than replace them. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

generative interpretation is meant to complement dictionaries, precedent, and human judgment rather than replace them. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that LLMs may offer more current and domain-specific usage evidence than static general-purpose dictionaries. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

LLMs may offer more current and domain-specific usage evidence than static general-purpose dictionaries. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that users should query multiple LLMs so that one model's biases or errors do not control the inquiry. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

users should query multiple LLMs so that one model's biases or errors do not control the inquiry. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that prompts and responses should be documented so an interpretive inquiry can be scrutinized and replicated. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

prompts and responses should be documented so an interpretive inquiry can be scrutinized and replicated. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that LLM outputs should remain advisory rather than determinative in legal decision-making. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

LLM outputs should remain advisory rather than determinative in legal decision-making. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that human oversight is necessary to identify bias, fabrication, and other errors in AI-generated interpretive evidence. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

human oversight is necessary to identify bias, fabrication, and other errors in AI-generated interpretive evidence. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Practical Applications and Best Practices,” that case studies can test where LLM language evidence aids courts and where the method encounters limits. The discussion situates this proposition within the practical examples and safeguards proposed for responsible use of generative interpretation. This is significant because it converts a theoretical method into a reproducible, human-supervised legal practice. It connects to cross-model verification, prompt documentation, replicability, human oversight, hallucination, advisory evidence.

case studies can test where LLM language evidence aids courts and where the method encounters limits. This matters because it converts a theoretical method into a reproducible, human-supervised legal practice.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that lawyers may need new competence in prompt design and AI-assisted interpretation to prepare and argue cases effectively. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

lawyers may need new competence in prompt design and AI-assisted interpretation to prepare and argue cases effectively. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.

legal profession
prompt engineering
judicial decision-making
litigation costs
human judgment
algorithmic bias

The essay frames these effects as possibilities and open questions, not as measured causal outcomes.

The online essay's “Impact on the Legal System” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that judges may gain access to broader language data that could support more informed and consistent decisions. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

judges may gain access to broader language data that could support more informed and consistent decisions. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that clients could benefit if greater interpretive consistency reduces uncertainty and litigation costs. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

clients could benefit if greater interpretive consistency reduces uncertainty and litigation costs. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that adoption of generative interpretation risks overreliance on tools whose outputs remain imperfect. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

adoption of generative interpretation risks overreliance on tools whose outputs remain imperfect. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that legal systems must balance AI assistance against the continuing need for human discretion and empathy. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

legal systems must balance AI assistance against the continuing need for human discretion and empathy. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Impact on the Legal System,” that LLM-assisted interpretation must be designed so that it does not reproduce or amplify existing legal-system biases. The discussion situates this proposition within the essay's assessment of possible effects on lawyers, judges, clients, and legal judgment. This is significant because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy. It connects to legal profession, prompt engineering, judicial decision-making, litigation costs, human judgment, algorithmic bias.

LLM-assisted interpretation must be designed so that it does not reproduce or amplify existing legal-system biases. This matters because it connects interpretive technology to professional competence, litigation cost, consistency, and legitimacy.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that generative interpretation forms part of a broader University of Alabama initiative on AI, law, and legal education. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

generative interpretation forms part of a broader University of Alabama initiative on AI, law, and legal education. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.

AI legal education
AI for Lawyers
systemic AI risk
consumer legal tools
reasonable person
access to justice

The institutional initiatives and research directions are agenda-setting; the essay does not report completed evaluations of all proposed tools or courses.

The online essay's “Broader Implications and Future Research” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that an AI for Lawyers course can teach students by having them build practical tools for legal tasks. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

an AI for Lawyers course can teach students by having them build practical tools for legal tasks. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that Arbel's AI-and-law agenda joins systemic-risk governance with constructive integration of AI into courts. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

Arbel's AI-and-law agenda joins systemic-risk governance with constructive integration of AI into courts. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that consumer-facing AI tools may help people understand and manage complex legal documents. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

consumer-facing AI tools may help people understand and manage complex legal documents. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that AI tools can be developed to simulate how juries and ordinary people understand the reasonable person. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

AI tools can be developed to simulate how juries and ordinary people understand the reasonable person. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that generative interpretation could improve accuracy, consistency, and accessibility while reducing litigation. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

generative interpretation could improve accuracy, consistency, and accessibility while reducing litigation. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Broader Implications and Future Research,” that the promise of AI-assisted interpretation must be evaluated alongside ethical questions about machine influence on legal judgment. The discussion situates this proposition within the broader research and teaching agenda for AI in law. This is significant because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment. It connects to AI legal education, AI for Lawyers, systemic AI risk, consumer legal tools, reasonable person, access to justice.

the promise of AI-assisted interpretation must be evaluated alongside ethical questions about machine influence on legal judgment. This matters because it locates generative interpretation within a larger program spanning pedagogy, systemic risk, access tools, and models of legal judgment.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that the Snell trampoline dispute helped move generative interpretation from academic proposal toward mainstream legal thought. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

the Snell trampoline dispute helped move generative interpretation from academic proposal toward mainstream legal thought. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.

legal interpretation
responsible adoption
methodological refinement
court innovation
human values
access to justice

The conclusion is forward-looking and normative; adoption rates, accuracy gains, and distributional effects remain to be tested.

The online essay's “Conclusion” section directly develops this proposition.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that integrating AI into legal interpretation may make doctrine more responsive to real-world language usage. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

integrating AI into legal interpretation may make doctrine more responsive to real-world language usage. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that AI-assisted interpretation challenges lawyers and judges to reconsider foundational assumptions about legal reasoning. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

AI-assisted interpretation challenges lawyers and judges to reconsider foundational assumptions about legal reasoning. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that courts should monitor impacts and refine methodology as generative interpretation gains traction. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

courts should monitor impacts and refine methodology as generative interpretation gains traction. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that scholarly design and judicial experimentation can develop symbiotically rather than along separate tracks. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

scholarly design and judicial experimentation can develop symbiotically rather than along separate tracks. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that future adoption may involve courts, law-school curricula, and specialized legal-technology products. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

future adoption may involve courts, law-school curricula, and specialized legal-technology products. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that technological potential must remain balanced with the human elements central to legal practice. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

technological potential must remain balanced with the human elements central to legal practice. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.


Professor Yonathan Arbel claims, in the article “Thinking the Unthinkable: AI in the Service of Justice,” an unpaginated online essay, under “Conclusion,” that the ultimate measure of generative interpretation is whether it makes the legal system more accurate, efficient, fair, and just. The discussion situates this proposition within the conclusion's account of responsible adoption and the future of legal interpretation. This is significant because it makes fairness and justice—not technological novelty—the criterion for continued development. It connects to legal interpretation, responsible adoption, methodological refinement, court innovation, human values, access to justice.

the ultimate measure of generative interpretation is whether it makes the legal system more accurate, efficient, fair, and just. This matters because it makes fairness and justice—not technological novelty—the criterion for continued development.