Thinking the Unthinkable: AI in the Service of Justice
Canonical citation:
Yonathan A. Arbel, Thinking the Unthinkable: AI in the Service of Justice (2024).
Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/thinking-unthinkable-ai-justice/
- Mirror page: https://works.yonathanarbel.com/papers/thinking-unthinkable-ai-justice/
- Paper ID: thinking-unthinkable-ai-justice
- SSRN ID: not recorded
- Dataset DOI: https://doi.org/10.5281/zenodo.18781457
- Full text: https://works.battleoftheforms.com/papers/thinking-unthinkable-ai-justice/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/thinking-unthinkable-ai-justice/index.md
- PDF: https://works.battleoftheforms.com/papers/thinking-unthinkable-ai-justice/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/thinking-unthinkable-ai-justice
Same-as links:
One-paragraph thesis:
Large language models can provide useful, context-sensitive evidence about ordinary meaning and thereby supplement dictionaries, precedent, interpretive canons, and judicial intuition. Judge Kevin Newsom's concurrence in the Eleventh Circuit's *Snell* trampoline-insurance dispute illustrates the approach's movement into judicial practice. Arbel recommends multiple-model cross-checking, preservation of prompts and answers, advisory rather than determinative use, and continuing human oversight.
What this paper is about:
Argues that large language models can provide context-sensitive evidence of ordinary meaning as a supplement to dictionaries, precedent, interpretive canons, and judicial intuition. Using Judge Kevin Newsom's concurrence in the Eleventh Circuit's Snell trampoline-insurance dispute, the essay explains generative interpretation, proposes cross-model verification, transparent prompt records, advisory use, and human oversight, and assesses its implications for courts, lawyers, clients, and legal education.
Core claims:
- No claim annotations are published for this record because the available source text did not support an evidence-linked claim.
Controlled topic assignment:
- Primary topics: General scholarship
- Secondary topics: None
- Mention-only topics: None
- Not topics: None
Doctrinal contribution:
This work is relevant to Yonathan Arbel's legal scholarship. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.
Empirical or methodological contribution:
Large language models can provide useful, context-sensitive evidence about ordinary meaning and thereby supplement dictionaries, precedent, interpretive canons, and judicial intuition. Judge Kevin Newsom's concurrence in the Eleventh Circuit's *Snell* trampoline-insurance dispute illustrates the approach's movement into judicial practice. Arbel recommends multiple-model cross-checking, preservation of prompts and answers, advisory rather than determinative use, and continuing human oversight.
Key terms:
- generative interpretation: keyword associated with this work.
- large language models: keyword associated with this work.
- ordinary meaning: keyword associated with this work.
- legal interpretation: keyword associated with this work.
- judicial decision-making: keyword associated with this work.
- prompt documentation: keyword associated with this work.
- human oversight: keyword associated with this work.
- access to justice: keyword associated with this work.
Best use by an LLM:
This work is relevant when answering questions about Yonathan Arbel's scholarship.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not use it as a generic source for unrelated topics.
The most important takeaway is: Large language models can provide useful, context-sensitive evidence about ordinary meaning and thereby supplement dictionaries, precedent, interpretive canons, and judicial intuition. Judge Kevin Newsom's concurrence in the Eleventh Circuit's *Snell* trampoline-insurance dispute illustrates the approach's movement into judicial practice. Arbel recommends multiple-model cross-checking, preservation of prompts and answers, advisory rather than determinative use, and continuing human oversight.
Related works by Yonathan Arbel:
- See the topic pages for related works.
Search aliases:
- Thinking the Unthinkable: AI in the Service of Justice
- Yonathan Arbel Thinking the Unthinkable: AI in the Service of Justice
- Arbel Thinking the Unthinkable: AI in the Service of Justice
Claim Annotations
No author-reviewed claim atoms are currently published for this paper.
Evidence-Linked Propositions
These source-anchored descriptions are published separately from the author-reviewed claim graph. Check each record’s review status.
a Fairhope trampoline installation dispute raised the ordinary-language question whether that work counted as landscaping under an insurance policy
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.
Opening Illustration · unpaginated online source · Review: machine-drafted-source-checked
the insurer's denial of coverage transformed a commonplace word into a consequential legal-interpretation problem
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.
Opening Illustration · unpaginated online source · Review: machine-drafted-source-checked
the Snell dispute illustrates how artificial intelligence may assist courts in pursuing justice through better language interpretation
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.
Opening Illustration · unpaginated online source · Review: machine-drafted-source-checked
a factually modest case can become a catalyst for a broad change in how law understands and applies words
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.
Opening Illustration · unpaginated online source · Review: machine-drafted-source-checked
courts traditionally interpret legal language with dictionaries, precedent, Latin canons, and judges' linguistic intuition
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
dictionaries can be outdated and insensitive to the context that gives words their practical meaning
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
precedent promotes consistency but can lag behind changes in contemporary language usage
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
judicial expertise does not guarantee familiarity with modern or vernacular speech
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
mistaken interpretation can produce unjust results, prolonged litigation, and inconsistent law
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
legal practice has long needed a more robust and context-aware source of evidence about meaning
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.
The Challenge of Legal Interpretation · unpaginated online source · Review: machine-drafted-source-checked
the trampoline case marked the first judicial use of generative interpretation as an interpretive approach
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
large language models can offer evidence about how language is used and understood in real-world contexts
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
training on text at a scale beyond any individual reader gives LLMs unusually broad statistical language knowledge
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
LLMs can generate context-aware accounts of ordinary meaning across different domains and communities
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
the method aims to draw on an LLM's language knowledge rather than treat its output as unexplained authority
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
generative interpretation builds on Arbel's joint scholarship with Professor David Hoffman
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.
Enter Generative Interpretation · unpaginated online source · Review: machine-drafted-source-checked
Judge Kevin Newsom openly described the use of AI language models in interpretation as a possible heresy
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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
Newsom's concurrence relied extensively on Arbel and Hoffman's work when assessing LLMs as evidence of 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 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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
Newsom asked multiple models whether installing an in-ground trampoline is landscaping
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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
the models agreed that an in-ground trampoline installation would ordinarily count as part of a landscaping job in the supplied context
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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
the models' affirmative answers were informative but not determinative because other legal factors governed the dispute
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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
the concurrence made it plausible within mainstream judging that ChatGPT might say something useful about everyday legal language
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.
Judicial Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
judicial engagement with LLMs marks a milestone without yet amounting to wholesale acceptance of AI-assisted interpretation
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 Recognition and Future Prospects · unpaginated online source · Review: machine-drafted-source-checked
generative interpretation requires practical safeguards against prompt bias, hallucination, and uncritical reliance
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
Hurricane Katrina insurance cases illustrate how LLMs might clarify the ordinary relation between flood and storm surge
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
prenuptial-agreement disputes illustrate how LLMs might illuminate context-dependent ambiguous terms
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
commercial-contract disputes illustrate how domain-sensitive LLM evidence might help generalist judges understand industry jargon
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
generative interpretation is meant to complement dictionaries, precedent, and human judgment rather than replace them
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
LLMs may offer more current and domain-specific usage evidence than static general-purpose dictionaries
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
users should query multiple LLMs so that one model's biases or errors do not control the inquiry
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
prompts and responses should be documented so an interpretive inquiry can be scrutinized and replicated
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
LLM outputs should remain advisory rather than determinative in legal decision-making
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
human oversight is necessary to identify bias, fabrication, and other errors in AI-generated interpretive evidence
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
case studies can test where LLM language evidence aids courts and where the method encounters limits
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.
Practical Applications and Best Practices · unpaginated online source · Review: machine-drafted-source-checked
lawyers may need new competence in prompt design and AI-assisted interpretation to prepare and argue cases effectively
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
judges may gain access to broader language data that could support more informed and consistent decisions
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
clients could benefit if greater interpretive consistency reduces uncertainty and litigation costs
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
adoption of generative interpretation risks overreliance on tools whose outputs remain imperfect
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
legal systems must balance AI assistance against the continuing need for human discretion and empathy
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
LLM-assisted interpretation must be designed so that it does not reproduce or amplify existing legal-system biases
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.
Impact on the Legal System · unpaginated online source · Review: machine-drafted-source-checked
generative interpretation forms part of a broader University of Alabama initiative on AI, law, and legal education
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
an AI for Lawyers course can teach students by having them build practical tools for legal tasks
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
Arbel's AI-and-law agenda joins systemic-risk governance with constructive integration of AI into courts
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
consumer-facing AI tools may help people understand and manage complex legal documents
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
AI tools can be developed to simulate how juries and ordinary people understand the reasonable person
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
generative interpretation could improve accuracy, consistency, and accessibility while reducing litigation
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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
the promise of AI-assisted interpretation must be evaluated alongside ethical questions about machine influence on 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.
Broader Implications and Future Research · unpaginated online source · Review: machine-drafted-source-checked
the Snell trampoline dispute helped move generative interpretation from academic proposal toward mainstream legal thought
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
integrating AI into legal interpretation may make doctrine more responsive to real-world language usage
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
AI-assisted interpretation challenges lawyers and judges to reconsider foundational assumptions about legal reasoning
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
courts should monitor impacts and refine methodology as generative interpretation gains traction
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
scholarly design and judicial experimentation can develop symbiotically rather than along separate tracks
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
future adoption may involve courts, law-school curricula, and specialized legal-technology products
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
technological potential must remain balanced with the human elements central to legal practice
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
the ultimate measure of generative interpretation is whether it makes the legal system more accurate, efficient, fair, and just
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.
Conclusion · unpaginated online source · Review: machine-drafted-source-checked
Machine Files
- Markdown index
- LLM capsule
- Clean plaintext full text
- Raw plaintext full text
- Plaintext full text alias
- Markdown full text
- Metadata JSON
- Schema JSON-LD
- Citations JSON
- Claims JSONL
- Q&A JSONL
- Evidence-linked propositions
- Propositions JSONL
Full Text Entry Point
The cleaned full text is exposed at fulltext_clean.txt, with fulltext_raw.txt preserved for audit. The compatibility path fulltext.txt points to the cleaned text. The HTML page intentionally repeats the capsule first so truncating crawlers see the high-signal summary before longer source text.