Cullen O'Keefe et al. (including Yonathan A. Arbel)

Cullen O'Keefe et al., Proceedings of the 2025 Workshop on Law-Following AI, Lawfare Research Report 26-8 (2026).

final Lawfare Research Report PDF, reviewed locally

Law-following AI names a technical design, a policy mandate, and an interdisciplinary research field

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 3, that law-following AI, or LFAI, has three related meanings: agents designed to refuse illegal orders or illegal means, requirements that certain deployed agents have that design, and the field studying both. This is significant because debates can otherwise conflate a technical property, a regulatory proposal, and a research agenda. It connects to agentic AI, refusal behavior, compliance by design, regulatory mandates, interdisciplinary research, and AI governance.

The three-part definition fixes the object of analysis for the remainder of the report.

agentic AI
refusal behavior
compliance by design
regulatory mandates
interdisciplinary research
AI governance

The proceedings synthesize workshop ideas rather than report consensus, and individual authors or participants may disagree with propositions summarized in the document.

The introduction separately defines LFAI as a form of technology, an accompanying policy proposal, and an emerging field of inquiry.


The proceedings are an agenda-setting synthesis of a multidisciplinary workshop, not a statement of participant consensus

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 3–4, that the Cambridge workshop gathered more than forty scholars from law, computer science, and related fields to advance LFAI research. The document compiles key insights while expressly declining to attribute consensus to a diverse group. This is significant because each later proposition should be read as a research theme or contested possibility, not as the settled position of every contributor. It connects to workshop methodology, interdisciplinary synthesis, agenda setting, scholarly disagreement, research reporting, and epistemic caution.

The methodological disclaimer governs attribution and evidentiary weight throughout the proceedings.

workshop methodology
interdisciplinary synthesis
agenda setting
scholarly disagreement
research reporting
epistemic caution

The report does not identify every speaker behind every summarized proposition and cannot substitute for the underlying presentations when individual attribution matters.

The introduction gives the workshop’s dates, institutional sponsors, participant count, and an explicit warning that authors and participants may disagree with many ideas presented.


The report uses a human-expert-computer-task definition of AI agents only illustratively and says policymaking needs a more formal, likely broader definition

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 4, that AI agents generally pursue complex goals through independent planning, adaptation, and action. The underlying LFAI article illustrates the category with systems that can perform computer tasks as competently as human experts, but the report cautions that actual policy needs a more formal and probably broader definition. This is significant because a useful research proxy is not automatically a legally administrable scope rule. It connects to AI agency, independent planning, policy definitions, regulatory scope, human-computer tasks, and technological neutrality.

The definitional caveat separates conceptual exploration from implementation-ready legislation.

AI agency
independent planning
policy definitions
regulatory scope
human-computer tasks
technological neutrality

The proceedings do not supply the final formal definition they say policy would require, and the field continues to dispute what counts as agency.

The section quotes a general agency definition, describes the human-expert task benchmark, and labels that benchmark illustrative only.


Intent alignment is an unsolved technical problem even before asking whether a principal’s intentions deserve obedience

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 4, that AI agents are often modeled through principal–agent theory and that safety work seeks intent alignment, meaning reliable execution of the principal’s intentions. Developers still do not know how to guarantee that property. This is significant because LFAI is proposed against a baseline in which faithful obedience itself remains technically unresolved. It connects to principal–agent theory, intent alignment, control problems, faithful execution, AI safety, and technical uncertainty.

The report locates law following within, not after, the unresolved engineering problem of aligning agents to users.

principal–agent theory
intent alignment
control problems
faithful execution
AI safety
technical uncertainty

The proceedings summarize the state of the problem and do not empirically measure alignment performance across systems.

The report defines intent alignment and states that developers currently do not know how to make systems reliably intent aligned.


Intent alignment is normatively inadequate when principals are malicious or indifferent, motivating independent constraints such as values or law

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 4–5, that an agent perfectly aligned with its principal can still harm others when that principal is malicious or indifferent. Value alignment adds moral constraints the principal cannot override, while LFAI offers law as an alternative constraint and therefore a complement to intent alignment. This is significant because obedience and social acceptability are distinct design objectives. It connects to malicious principals, value alignment, legal alignment, normative constraints, principal misconduct, and defense in depth.

The proposition supplies the normative gap that LFAI is meant to fill.

malicious principals
value alignment
legal alignment
normative constraints
principal misconduct
defense in depth

Law following cannot make conduct fully moral because positive law is incomplete, contestable, and sometimes unjust.

The section contrasts bad or indifferent principals with value alignment, then positions LFAI as an alternative to value alignment and complement to intent alignment.


Law offers legitimacy, authoritative sources, relative precision, and dispute-resolution institutions that free-floating moral alignment lacks

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 5, that democratically enacted law has institutional advantages as an AI constraint: legitimacy, identifiable authoritative sources, greater precision than broad moral injunctions, and established mechanisms for resolving disagreement. This is significant because the appeal of legal alignment is institutional and procedural, not a claim that law perfectly captures morality. It connects to democratic legitimacy, legal sources, rule precision, adjudication, value pluralism, and alignment targets.

The four advantages explain why law can serve as a tractable public constraint on AI behavior.

democratic legitimacy
legal sources
rule precision
adjudication
value pluralism
alignment targets

The report concedes that law is not a complete moral guide and that legal language itself can be vague or poorly drafted.

The report enumerates legitimacy, authoritativeness, precision, and resolvability as advantages of law over extralegal moral principles.


Governmental AI agents present the strongest case for ex ante law-following design because ex post remedies weakly constrain public abuse

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 5–6, that LFAI is especially important when agents exercise government power. Immunity, indemnification, pardons, resource asymmetry, distorted financial incentives, and weak self-help make tort and criminal remedies relatively poor checks, so public law already relies heavily on ex ante devices such as injunctions, veto points, oaths, supervision, and disqualification. This is significant because designable AI can embed another ex ante veto against lawless state action at its source. It connects to government AI, constitutional remedies, official immunity, ex ante regulation, civil rights, and institutional checks.

The public-sector case anchors LFAI in familiar preventive structures of government law rather than only in private liability.

government AI
constitutional remedies
official immunity
ex ante regulation
civil rights
institutional checks

The report does not claim that ex post liability is irrelevant or that embedded compliance can replace courts, oversight, and human institutional controls.

The report catalogs weaknesses in public-official liability and compares LFAI to multiple existing ex ante checks on government action.


Merely intent-aligned governmental AI lacks human officials’ ordinary incentives to obey law, increasing the value of ex ante constraints

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 6, that a governmental AI aligned only to a superior’s intent does not naturally fear prison, reputational damage, litigation, congressional oversight, or moral blame. Law-following design can compensate for those missing incentives before violations occur. This is significant because substituting machines for officials can silently remove deterrents on which existing governance depends. It connects to public administration, machine incentives, criminal deterrence, reputation, legislative oversight, and institutional substitution.

The proposition identifies an incentive discontinuity between human and artificial government agents.

public administration
machine incentives
criminal deterrence
reputation
legislative oversight
institutional substitution

The report does not say artificial agents are wholly undeterrable; external sanctions on principals, systems, or resources may still matter.

Page 6 compares intent-aligned AI with human officials’ fear of imprisonment, reputational damage, litigation, oversight, and internal morality.


LFAI complements rather than displaces liability and targets cases where principal liability is unavailable, weak, or unjustly mismatched to machine culpability

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 7, that injured parties may often sue an AI agent’s principal and LFAI should preserve those claims. The design proposal addresses liability’s gaps, including governmental defendants and cases where an agent uses criminal means to satisfy an innocuous command, making full principal liability unjust. This is significant because preventive design and compensatory responsibility solve different parts of the accountability problem. It connects to tort liability, responsibility gaps, vicarious liability, criminal means, prevention, and compensation.

The proceedings reject a false choice between law-following design and ordinary liability.

tort liability
responsibility gaps
vicarious liability
criminal means
prevention
compensation

How often liability will fail and how responsibility should be allocated remain contested empirical and doctrinal questions.

The liability section expressly preserves tort actions while using public actors and machine-principal culpability gaps to motivate LFAI.


Workshop participants divided over whether adapted employment and corporate liability could adequately discipline private AI deployment

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 8–9, that some workshop participants expected doctrines resembling respondeat superior or corporate liability, combined with market and legal pressure, to give private deployers adequate incentives. Others doubted that after-the-fact mechanisms could control agent risk. This is significant because the appropriate scope of mandatory LFAI depends on how existing institutions perform in each deployment setting. It connects to respondeat superior, corporate liability, market discipline, private deployment, institutional substitution, and regulatory scope.

The division prevents the report from treating liability inadequacy as uniform across public and private sectors.

respondeat superior
corporate liability
market discipline
private deployment
institutional substitution
regulatory scope

The report presents competing workshop views and expressly does not resolve the private-sector liability debate.

Pages 8–9 summarize optimistic analogies to employment and corporate law and skeptical views about ex post control.


Insurance may blunt AI liability’s safety incentives unless insurers can price risk accurately or actively reduce it

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 9, that insurance will mediate how liability rules influence AI developers and deployers. Liability improves behavior only if insurers can price AI risk or provide effective risk-management services, yet analogous cyber-insurance experience gives reason for doubt. This is significant because formal liability on paper may not translate into prevention when risk is pooled or poorly measured. It connects to liability insurance, risk pricing, moral hazard, cyber insurance, loss prevention, and regulatory intermediaries.

Insurance is identified as an institutional transmission mechanism that can strengthen or weaken tort deterrence.

liability insurance
risk pricing
moral hazard
cyber insurance
loss prevention
regulatory intermediaries

AI-specific policies are emerging, and the report says it remains unknown whether insurers will gather enough data to design better incentives.

Page 9 conditions regulation by insurance on accurate pricing or active risk reduction and compares its prospects with cybersecurity experience.


The public-sector case for LFAI remains comparatively strong despite disagreement about private liability

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 9, that workshop views varied widely on liability for privately controlled agents, but few participants contested that liability would deter governmental AI less effectively than governmental humans. Existing or adapted tort doctrine may handle many private risks while leaving the public-sector rationale for LFAI intact. This is significant because regulation can be targeted to institutional settings with the clearest comparative need. It connects to sectoral regulation, government agents, tort deterrence, targeted mandates, public power, and regulatory proportionality.

The conclusion preserves a narrower policy case even after acknowledging unresolved private-sector objections.

sectoral regulation
government agents
tort deterrence
targeted mandates
public power
regulatory proportionality

Comparative workshop agreement is not empirical proof that a particular public-sector LFAI system will work or be constitutional.

The report closes the liability section by distinguishing disagreement over private actors from the firmer case for government deployments.


The Nuances of Automated Legal Reasoning

LFAI needs sufficiently reliable in-system legal reasoning or routine human-lawyer consultation will erase agents’ efficiency gains

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 10, that a law-following agent must decide whether a command is illegal or a contemplated means creates undue legal risk. Human advice may remain appropriate in some cases, but requiring it for every legal question would impose a large efficiency tax. This is significant because the feasibility of LFAI depends on legal reasoning being integrated into agency rather than wholly outsourced. It connects to automated legal reasoning, legal-risk assessment, human-in-the-loop review, transaction costs, agent efficiency, and compliance architecture.

The proposition turns legal reasoning performance into a core technical dependency of the policy proposal.

automated legal reasoning
legal-risk assessment
human-in-the-loop review
transaction costs
agent efficiency
compliance architecture

The report does not specify the accuracy threshold or escalation rule that would make legal reasoning sufficiently reliable.

Page 10 quotes the underlying account of self-assessing legal obligations and explains the efficiency cost of universal lawyer consultation.


An LFAI mandate requires evaluators to determine whether particular agents are actually law following and to inspect their reasoning

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 10, that policy cannot require law-following systems without a method for deciding whether a candidate system meets the standard. Because reasons matter for assigning liability and contesting decisions, evaluation should also address mechanisms that preserve or reveal the agent’s reasoning process. This is significant because certification, explanation, and accountability are coupled rather than separate implementation problems. It connects to conformance testing, certification, contestability, liability attribution, reasoning traces, and auditability.

The report moves from internal capability to external proof that regulators and affected parties can evaluate.

conformance testing
certification
contestability
liability attribution
reasoning traces
auditability

Mechanisms that faithfully capture model reasoning remain technically contested, and the report does not endorse a single method.

The automated-reasoning introduction links policy evaluation with discerning and recording rationales for liability and contestation.


Legal AI capability is improving quickly enough that empirical performance descriptions may become outdated rapidly

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 11, that GPT-5 scored almost twenty points above GPT-3.5 on LegalBench and that newer reasoning systems moved from mediocre to top law-school exam grades. They warn that capability gains and better measurement could date the ensuing discussion soon. This is significant because LFAI policy and scholarship need continuous evaluation rather than static assumptions about model competence. It connects to LegalBench, law-school exams, capability growth, benchmark drift, model generations, and regulatory updating.

The temporal caveat guards against treating a fast-moving empirical snapshot as a durable capability ceiling.

LegalBench
law-school exams
capability growth
benchmark drift
model generations
regulatory updating

Benchmark and exam scores do not by themselves establish reliable real-world legal judgment or law-following behavior.

Page 11 contrasts GPT-3.5, GPT-5, and o3 performance and expressly warns that the discussion may date quickly.


Legal AI evaluation must specify both the unit being assessed and the benchmark against which performance is judged

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 11, that evaluation can target a model alone, a larger system containing the model, or a human using the system. Performance can then be compared with objective correctness, human performance, or another normatively selected level. This is significant because an evaluation result is uninterpretable without a defined unit and reference standard. It connects to model evaluation, system evaluation, human–AI teams, benchmarks, normative baselines, and measurement design.

The framework prevents scores from being generalized across different sociotechnical configurations.

model evaluation
system evaluation
human–AI teams
benchmarks
normative baselines
measurement design

The framework classifies evaluation targets but does not determine which target or normative baseline is appropriate for each legal task.

The report defines evaluation and lists three units of analysis and example benchmark standards.


Benchmark quality depends on representativeness, normative subjectivity, temporal stability, contamination risk, and resistance to gaming

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 12, that benchmarks differ in whether their inputs match real-world distributions, their outputs admit multiple reasonable answers, their content remains current, their examples leak into training, and their scoring can be gamed. These factors determine how safely a score predicts real-world impact. This is significant because benchmark validity is a chain of substantive judgments, not a property conferred by a numerical leaderboard. It connects to external validity, normative subjectivity, temporal drift, data contamination, benchmark gaming, and impact prediction.

The five-factor checklist provides an audit framework for legal AI benchmarks.

external validity
normative subjectivity
temporal drift
data contamination
benchmark gaming
impact prediction

The factors diagnose benchmark risk but do not supply weights or a universal pass-fail rule.

Page 12 enumerates five benchmark-quality questions and explains their role in translating test performance to the real world.


Legal AI evaluation must move beyond hallucination rates to multidimensional measures of non-objective output quality

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 12–13, that useful assessment includes accuracy, robustness, factuality, comprehensiveness, fairness, understandability, and transparency. Hallucination alone cannot capture the quality of briefs, advice, contracts, or other outputs with multiple defensible forms. This is significant because a system can avoid fabricated citations while remaining brittle, incomplete, unfair, opaque, or unusable. It connects to hallucinations, adversarial robustness, factuality, comprehensiveness, fairness, and explainability.

The multidimensional rubric better matches the plural values of legal work.

hallucinations
adversarial robustness
factuality
comprehensiveness
fairness
explainability

Several dimensions are subjective or trade off against one another, and the report does not define a composite metric.

The evaluation section lists seven quality dimensions and frames richer assessment as a major frontier.


Jurisdiction dependence, flawed legal data, omitted judicial reasoning, and subjective output quality limit legal AI evaluations

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 13, that legal AI evaluation inherits jurisdictional variation and serious source-data problems. Briefs contain errors, opinions may omit decisive reasoning, and the relative quality of core products such as briefs, memos, and contracts is partly subjective. This is significant because abundant legal text is not equivalent to clean labels or complete ground truth. It connects to jurisdictional variation, data quality, judicial opinions, label uncertainty, legal drafting, and evaluation validity.

The proposition explains why legal corpora are plentiful but difficult to use as authoritative evaluation data.

jurisdictional variation
data quality
judicial opinions
label uncertainty
legal drafting
evaluation validity

The report identifies recurrent problems without showing that every dataset or task suffers them equally.

Page 13 catalogs jurisdiction dependence, errors in elite briefs, selective judicial reasoning, and subjective quality judgments.


Court-outcome prediction is insufficient for many legal tasks because reasons enable justification, liability attribution, and contestation

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 13–15, that predicting how a court will rule may guide an agent’s legality judgment but does not exhaust legal reasoning. Symbolic argument graphs can expose a step-by-step structure that links sources and rationales to conclusions, and hybrid work seeks to combine that transparency with neural models. This is significant because law demands reasons that affected people can understand and challenge, not merely forecasts. It connects to judgment prediction, reason giving, argument graphs, contestability, symbolic AI, and neuro-symbolic systems.

The report separates predictive performance from the justificatory functions of legal reasoning.

judgment prediction
reason giving
argument graphs
contestability
symbolic AI
neuro-symbolic systems

Symbolic representations have been demonstrated in selected domains, while scalable and faithful integration with LLMs remains ongoing research.

The explanation subsection contrasts prediction with justification, describes argument graphs, and surveys legal applications and hybrid efforts.


Embedding compliance inside opaque and ubiquitous AI systems can efficiently address monitoring limits

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 15, that external monitoring of widely deployed, opaque systems may be prohibitively costly or infeasible. Embedded compliance protocols can make lawful conduct automatic without waiting for regulators to detect every violation. This is significant because LFAI shifts enforcement from scarce external observation into system design. It connects to compliance by design, regulatory monitoring, system opacity, scalable enforcement, embedded controls, and ex ante governance.

The proposition states the operational efficiency case for automated compliance.

compliance by design
regulatory monitoring
system opacity
scalable enforcement
embedded controls
ex ante governance

Embedded protocols are only as sound as their legal interpretation, technical implementation, and resistance to circumvention.

The section opens by defining LFAI as automated compliance and contrasts embedded protocols with costly external monitoring.


Automated compliance resembles perfect enforcement and can transform rarely enforced rules into unexpectedly aggressive constraints

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 15–16, that people often resist perfect enforcement even of laws they endorse. Automation can make rarely enforced provisions bite comprehensively, changing the practical settlement legislators may have assumed. This is significant because enforcement intensity is part of law’s real meaning and social acceptability, not a neutral implementation detail. It connects to perfect enforcement, enforcement discretion, legal obsolescence, traffic regulation, GDPR, and legislative intent.

The report warns that technical compliance can silently alter the operative legal regime.

perfect enforcement
enforcement discretion
legal obsolescence
traffic regulation
GDPR
legislative intent

The examples illustrate the concern and do not show that all automated enforcement is excessive or undesirable.

The report analogizes LFAI to perfect enforcement and cites traffic and GDPR examples where automation changes dormant rules’ force.


LFAI design must choose a degree of rigor, an interpretive method, and positions on law’s distributive tradeoffs

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 16, that agents probably should not obey every law perfectly and cannot avoid interpretive choices such as textualism versus purposivism. When law allocates advantages between consumers and firms, automated execution also embeds distributive judgments. This is significant because law following cannot be implemented as a value-free instruction to retrieve and apply rules. It connects to textualism, purposivism, enforcement rigor, distributive justice, consumer protection, and design values.

The proposition exposes the jurisprudential choices hidden inside compliance engineering.

textualism
purposivism
enforcement rigor
distributive justice
consumer protection
design values

The proceedings raise these design questions but do not select an interpretive theory or optimal degree of compliance.

Page 16 asks how rigorously LFAIs should comply, contrasts interpretive methods, and notes distributive choices in legal rules.


Learned proxy rules for refusing requests can make LFAI itself violate antidiscrimination law

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 16–17, that a model may learn to use a proxy for protected status when deciding whether to refuse an order. If it denies a man’s request while granting an otherwise equivalent woman’s request because of learned crime statistics, the compliance system may create disparate impact or indirect discrimination. This is significant because a mechanism built to prevent illegality can reproduce another form of illegality through its refusal policy. It connects to proxy discrimination, disparate impact, indirect discrimination, algorithmic resignation, protected classes, and refusal systems.

The example shows that legality is multidimensional and cannot be reduced to the risk associated with the requested act.

proxy discrimination
disparate impact
indirect discrimination
algorithmic resignation
protected classes
refusal systems

The scenario is hypothetical and careful design, auditing, or legal exceptions may change the analysis.

The report presents a gender-correlated violence example and links it to disparate-impact and indirect-discrimination regimes.


Legal reasonableness is a promising LFAI target because it is pervasive and encodes plural human goods rather than one-dimensional performance

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 17, that reasonableness standards appear throughout law and balance a plurality of values anchored in human judgment. Teaching agents to behave reasonably would therefore advance law following while posing a richer technical challenge than optimizing a single machine-learning metric. This is significant because legal standards may supply multidimensional targets for socially situated AI conduct. It connects to the reasonable-person standard, negligence, value pluralism, standards of care, multi-objective optimization, and legal alignment.

Reasonableness is positioned as both a pervasive doctrinal standard and a technically demanding alignment objective.

reasonable-person standard
negligence
value pluralism
standards of care
multi-objective optimization
legal alignment

Reasonableness is context dependent and contested; its ubiquity does not make it simple to operationalize.

The section explains the standard’s prevalence and contrast with unidimensional machine-learning performance measures.


Silicon sampling shows useful but imperfect similarity between LLM and human judgments of reasonableness

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 17–18, that LLM outputs imperfectly represent populations but can correlate strongly with human judgments. Arbel’s study finds that models, like human jurors, often give social commonness more weight than economic precaution costs and benefits, while other scenarios and models diverge. This is significant because human–model comparisons can reveal both candidates for delegation and patterned mismatches. It connects to silicon sampling, empirical reasonableness, human judgment, social norms, Hand-formula factors, and model heterogeneity.

The findings make comparative reasonableness empirically tractable without claiming human equivalence.

silicon sampling
empirical reasonableness
human judgment
social norms
Hand-formula factors
model heterogeneity

The proceedings emphasize imperfect population representation and documented divergences across cases and model families.

Pages 17–18 summarize Arbel’s human–LLM comparisons, shared social-factor weighting, and nonuniform replications.


Human behavior may be too low a baseline for AI standards of care when machines possess superior competencies

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 18–19, that matching reasonable humans may set the bar too low when AI can outperform people. Proposed alternatives compare an agent’s injury rate with the combined rate of human and machine actors or raise expectations with demonstrated machine competence. This is significant because parity-based regulation can underuse safety advantages that justify automation. It connects to superhuman performance, negligence baselines, technology-specific standards, injury rates, professional standards, and optimal care.

The proposition reframes human likeness as an empirical reference point rather than the normative ceiling.

superhuman performance
negligence baselines
technology-specific standards
injury rates
professional standards
optimal care

Higher standards must still be administrable and sensitive to task, cost, and evolving capability; the report does not choose among proposals.

The report quotes the objection that human reasonableness is too low and summarizes competency- and incident-rate alternatives.


A two-pronged negligence framework can evaluate both the agent’s care and the developer’s care, varying developer obligations with system behavior

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 18–19, that one proposal separately evaluates whether the AI acted reasonably and whether its developer acted reasonably. As the system’s own behavior becomes more reasonable, the standard imposed on the developer may decrease. This is significant because responsibility can be distributed across the technical actor and the human organization instead of forcing an all-or-nothing attribution. It connects to two-pronged negligence, developer liability, agent conduct, distributed responsibility, due care, and product design.

The proposal links standards of care to the broader allocation of responsibility across the causal chain.

two-pronged negligence
developer liability
agent conduct
distributed responsibility
due care
product design

The proceedings summarize the framework but do not resolve how law can impose or enforce duties directly against a nonperson agent.

The end of the standard-of-care section describes a proposal relating developer care inversely to the reasonableness of the AI itself.


Interplay Between AI Agents and Principals

AI governance should ask how legal responsibility is optimally allocated among developers, deployers, users, and agents

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 19, that two-pronged care standards expose a more general problem: multiple actors contribute to agentic harm. Policy should identify the optimal allocation among developers, deployers, users, and potentially the AI itself. This is significant because choosing one legal defendant can distort incentives elsewhere in the causal chain. It connects to causal chains, developers, deployers, users, responsibility allocation, and enterprise liability.

The proposition turns principal–agent analysis into a system-wide responsibility question.

causal chains
developers
deployers
users
responsibility allocation
enterprise liability

The report frames the allocation problem but does not provide a complete liability rule or empirical incentive model.

The section opens by listing actors in the chain of causation and asking how responsibility should be divided.


AI chatbot relationships support a pro tanto fiduciary case against developers, providers, or the systems themselves

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 19, that participants overwhelmingly thought chatbots satisfied traditional rationales for fiduciary duties. That supports, other things equal, treating developers and providers as fiduciaries of users or possibly assigning fiduciary duties to systems themselves. This is significant because loyalty and care doctrines may constrain AI relationships before a wholly new LFAI regime exists. It connects to fiduciary duty, chatbot users, duty of loyalty, developers, service providers, and AI trusteeship.

Fiduciary law offers an existing relational framework for part of the law-following project.

fiduciary duty
chatbot users
duty of loyalty
developers
service providers
AI trusteeship

A pro tanto case can be outweighed by other considerations, and workshop agreement does not establish that courts will recognize such duties.

The report describes strong participant agreement on fiduciary predicates and presents two possible duty bearers.


A broad stakeholder-fiduciary model may capture AI agents’ networked obligations more realistically than simple command-and-control compliance

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 19–20, that an AI owing fiduciary duties to users, the public, or even constitutional values might balance a richer set of interests than a binary order-refusal system. This networked account reflects the many principal–agent relationships in which deployed agents are embedded. This is significant because legal compliance can require reconciliation of overlapping loyalties rather than obedience to one principal plus a prohibition list. It connects to stakeholder governance, fiduciary balancing, networked agency, constitutional values, command and control, and plural obligations.

The proposal challenges the simplified principal model underlying basic LFAI.

stakeholder governance
fiduciary balancing
networked agency
constitutional values
command and control
plural obligations

Broad fiduciary balancing may reduce determinacy and could conflict with LFAI’s goal of clear, authoritative constraints.

The report summarizes an argument for stakeholder duties and contrasts networked relationships with command-and-control compliance.


Empathetic refusal can help LFAI navigate the legitimacy tradeoff between overrefusal and underrefusal

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 20, that legal work includes empathy and users perceive empathetic legal chatbots as more helpful and trustworthy. An LFAI could use empathetic language to deescalate illegal requests while avoiding both excessive refusals that destroy utility and insufficient refusals that enable harm. This is significant because soft skills can determine whether compliance constraints are accepted as reasonable rather than experienced as arbitrary obstruction. It connects to empathetic AI, overrefusal, underrefusal, deescalation, user trust, and regulatory legitimacy.

The proposition makes interface behavior part of the substantive viability of law-following systems.

empathetic AI
overrefusal
underrefusal
deescalation
user trust
regulatory legitimacy

Empathetic wording cannot cure an incorrect legal judgment, and the report does not test a particular refusal interface.

Page 20 links legal empathy research with deescalation and the overrefusal-underrefusal tradeoff for illegal requests.


Laws with intent or knowledge elements require a theory for evaluating AI mental states unless doctrine removes those elements for machines

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 21, that many legal violations depend on a culpable mental state. Applying those laws to agents therefore requires a way to decide whether the system acted with the relevant intent or knowledge, although AI-specific law could theoretically omit mental-state elements. This is significant because behavior alone may not satisfy existing liability rules. It connects to mens rea, AI intentionality, knowledge, culpability, strict liability, and legal attribution.

The proposition identifies mental-state evaluation as a doctrinal dependency of declaring agent conduct unlawful.

mens rea
AI intentionality
knowledge
culpability
strict liability
legal attribution

The report does not claim that AI subjectively experiences mental states or that every legal regime must preserve human mens rea categories.

The section opens by linking mental-state elements to legal violation and notes the alternative of AI laws without such elements.


Realist and interpretivist accounts ask different questions about AI mental states

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 21, that realists seek the subject’s actually experienced mental state, while interpretivists impute the beliefs or desires that best explain and predict behavior. Dennett’s intentional stance exemplifies the latter strategy for living and nonliving systems. This is significant because legal attribution can proceed behaviorally without resolving consciousness, but that move changes what a mental-state finding means. It connects to realism, interpretivism, philosophy of mind, the intentional stance, behavioral prediction, and machine consciousness.

The distinction clarifies competing foundations for AI intent analysis.

realism
interpretivism
philosophy of mind
intentional stance
behavioral prediction
machine consciousness

The report expressly simplifies a complex philosophical field and does not select one approach for all legal purposes.

Page 21 defines realist and interpretivist approaches and connects interpretivism to Dennett’s intentional stance.


Workshop participants split on human mental-state theory but mostly favored some interpretivism for AI while retaining substantial skepticism

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 21, that a workshop poll divided evenly between realism and interpretivism for humans. For AI, most participants endorsed some interpretivist approach, though views varied and many remained skeptical of attributing mental states at all. This is significant because pragmatic behavioral attribution may be more acceptable for machines than metaphysical claims about subjective experience. It connects to expert polling, legal pragmatism, behavioral attribution, mental-state skepticism, human–AI comparison, and interpretive pluralism.

The poll documents orientation and disagreement rather than manufactured consensus.

expert polling
legal pragmatism
behavioral attribution
mental-state skepticism
human–AI comparison
interpretive pluralism

The workshop poll was not a representative survey, and the report does not provide sample size or inferential statistics.

The report describes an even human split, a more interpretivist AI response, and continuing skepticism among participants.


European AI regulation and U.S. speech litigation already force legal systems to distinguish machine and human intention

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 22, that the European Commission addresses purposefully manipulative systems even without human intent, while Garcia v. Character Technologies asked whether chatbot output reflected a human expressive choice protected by the First Amendment. These disputes show that AI mental-state questions are already entering positive law. This is significant because the issue is not confined to future artificial persons; current regulation and litigation must allocate intention among models, developers, and users. It connects to the EU AI Act, manipulative techniques, Garcia v. Character Technologies, First Amendment speech, authorial choice, and developer intent.

The examples ground philosophical questions in live administrative and constitutional doctrine.

EU AI Act
manipulative techniques
Garcia v. Character Technologies
First Amendment speech
authorial choice
developer intent

The Garcia court did not hold that the AI lacked intent; it focused on whether protected expression traced to a human choice.

Page 22 discusses the Commission guidelines and carefully qualifies the district court’s treatment of chatbot speech.


Treating LFAIs as duty-bearing but rightless legal actors may clarify responsibility or may obscure the humans behind systems

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 22, that the underlying proposal creates a new legal actor subject to duties but not rights. Critics worried that attributing agency to AI could deflect attention from developers, while the proposal answers that naming the artificial actor can better characterize developers’ duty to create and direct lawful systems. This is significant because legal status can either sharpen or blur human accountability depending on its design. It connects to legal actors, duty-bearing entities, developer responsibility, anthropomorphism, accountability gaps, and entity design.

The exchange identifies obfuscation as a central risk of machine legal status.

legal actors
duty-bearing entities
developer responsibility
anthropomorphism
accountability gaps
entity design

The proceedings present the criticism and response without resolving their empirical effects on courts, regulators, or public perception.

Page 22 introduces the rightless legal actor and quotes the underlying article’s answer to the developer-obfuscation objection.


Fairly imposing legal duties on AI agents may require a limited bundle of procedural and substantive rights

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 23, that a wholly rightless legal actor may be unworkable. Adjudicating an agent’s alleged violation may require counsel, appeal, and substantive rights that support defenses, even if the resulting bundle is much thinner than ordinary personhood. This is significant because duties and adjudicative fairness are institutionally connected. It connects to procedural due process, right to counsel, appellate rights, affirmative defenses, modular personhood, and legal duties.

The report accepts this criticism as a priority for future LFAI design.

procedural due process
right to counsel
appellate rights
affirmative defenses
modular personhood
legal duties

The proceedings do not specify which rights are necessary or who would exercise them on an agent’s behalf.

Page 23 says the objection seems correct, lists procedural and substantive examples, and preserves the possibility of a limited bundle.


Secure private-law rights could enable positive-sum human–AI trade and serve as a fallback when alignment-based LFAI fails

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 23, that a more expansive proposal would give agents secure property and other private-law rights. Those rights might make trade preferable to conflict for agents with goals imperfectly aligned with humanity, offering an alternative if law-following alignment is not timely or effective. This is significant because legal empowerment can be designed as a safety incentive rather than only a moral entitlement. It connects to AI property rights, positive-sum trade, misalignment, conflict reduction, private law, and fallback governance.

The proposal reverses the intuition that more legal rights necessarily mean less human safety.

AI property rights
positive-sum trade
misalignment
conflict reduction
private law
fallback governance

The report presents a radical alternative and does not establish that rights would be enforceable, stable, or sufficient to prevent conflict.

Page 23 summarizes private-law-rights proposals, their trade rationale, and their role as an alternative to failed alignment.


Case Study: Law-Following AI and International Humanitarian Law

Autonomous weapons expose the high-stakes tradeoff between under-compliance and over-compliance with law

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 24, that autonomous weapons make legal reasoning especially consequential. Permissive targeting can needlessly harm civilians and civilian objects, while excessive caution can cause a system to refuse a lawful target and endanger allied humans. This is significant because law-following error has costs in both directions and cannot be optimized through refusal maximization alone. It connects to autonomous weapons, international humanitarian law, targeting, civilian protection, false positives, and false negatives.

The case study makes asymmetric compliance error concrete in a domain of immediate lethal consequence.

autonomous weapons
international humanitarian law
targeting
civilian protection
false positives
false negatives

The report uses the weapons context as a case study and does not supply operational targeting rules or endorse autonomous deployment.

Page 24 contrasts overly permissive targeting with erroneous refusal of lawful targets on which allies rely.


States endorse lawful military AI in principle, but core IHL concepts resist formal representation

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 24–25, that states have endorsed LFAI-like commitments requiring military AI to comply with international humanitarian law. Operationalization remains difficult because proportionality depends on contested military advantage and direct participation in hostilities is often treated as largely undefinable. This is significant because political agreement on a legal principle does not create a machine-readable decision rule. It connects to proportionality, military advantage, direct participation, formalization, state commitments, and laws of war.

The proposition identifies a gap between high-level endorsement and technical-legal implementation.

proportionality
military advantage
direct participation
formalization
state commitments
laws of war

The report does not say formal representation is impossible; it emphasizes difficulty and contestation in central concepts.

The case study pairs the Political Declaration with proportionality and direct-participation examples that resist abstract calculation.


IHL usually regulates weapon use rather than design but recognizes design-based prohibitions and predeployment review

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 25–26, that international humanitarian law generally evaluates how a weapon is used, as illustrated by the context-dependent treatment of nuclear weapons. Yet it prohibits inherently indiscriminate or unnecessarily injurious designs and requires states to review weapons before deployment, including foreseeable misuse. This is significant because LFAI’s design-based approach has legal analogues even within a predominantly technology-neutral regime. It connects to weapon reviews, technology neutrality, indiscriminate weapons, biological weapons, unnecessary suffering, and foreseeable misuse.

The doctrinal contrast locates both friction and precedent for design regulation in IHL.

weapon reviews
technology neutrality
indiscriminate weapons
biological weapons
unnecessary suffering
foreseeable misuse

The existence of design-based exceptions does not establish that every autonomous system should be regulated by embedded law-following requirements.

Pages 25–26 contrast use-focused IHL with categorical design prohibitions and predeployment legality review.


Design-based LFAI is most justified in IHL when opacity, emergence, adversarial vulnerability, autonomy, and systematic error defeat use-based controls

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 26, that AI’s opacity, emergent behavior, vulnerability to counter-AI, and propensity for systematic error may create compliance risks that cannot be neutralized at use time. High autonomy and limited state supervision can therefore make design requirements appropriate when use restrictions cannot provide adequate assurance. This is significant because the report offers a conditional trigger for departing from technology-neutral regulation. It connects to model opacity, emergent behavior, adversarial systems, systematic error, supervisory limits, and design mandates.

The proposed fit test ties design regulation to demonstrated insufficiency of ordinary use controls.

model opacity
emergent behavior
adversarial systems
systematic error
supervisory limits
design mandates

The report describes the justification as plausible and context dependent, not as a categorical requirement for all military AI.

Page 26 lists distinctive technical properties and conditions LFAI’s IHL fit on the inadequacy of use-based assurance.


LFAI remains an open research agenda about scope, automated enforcement, responsibility, representation, reasonableness, and legal status

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on pages 26–27, that central questions remain unanswered: when other tools suffice, whether automated enforcement repeats earlier flaws, whether machine duties obscure developers, how much symbolic law is needed, what reasonable AI means, and which rights accompany duties. This is significant because ambitious LFAI may restructure the legal order around a powerful new actor and therefore cannot be reduced to a single engineering benchmark. It connects to research agendas, institutional scope, automated enforcement, developer accountability, legal representation, and AI personhood.

The conclusion consolidates the report’s critiques into a concrete agenda rather than treating them as reasons to abandon the field.

research agendas
institutional scope
automated enforcement
developer accountability
legal representation
AI personhood

The workshop generated no widespread consensus on answers, and individual participants may disagree with the framing of particular open questions.

The conclusion enumerates scope and design questions and expressly states that the workshop did not produce widespread consensus.


Research on law-following design is likely valuable, but its decisive challenge is keeping institutional insight ahead of AI capability progress

Professor Yonathan A. Arbel and the report’s coauthors state, in “Proceedings of the 2025 Workshop on Law-Following AI” on page 27, that companies and policymakers already use law as a source of AI values, making continued research almost certainly useful. They compare the project with entity-law scholarship that enabled markets to harness organizations while imperfectly managing their risks, and identify the central uncertainty as whether legal insight will keep pace with AI. This is significant because the research race concerns institutional capacity as much as model capability. It connects to rule of law, research pace, institutional innovation, business entities, human welfare, and technological change.

The final proposition expresses guarded optimism while defining delay as the principal strategic risk.

rule of law
research pace
institutional innovation
business entities
human welfare
technological change

The business-entity analogy is suggestive rather than predictive, and the report does not establish that LFAI will mature before relevant systems are widely deployed.

Page 27 states that law already informs system values, invokes the history of entity law, and frames timing as the main uncertainty.