Yonathan A. Arbel, Ryan Copus, Kevin Frazier, Noam Kolt, Alan Z. Rozenshtein, Peter N. Salib, Chinmayi Sharma, and Matthew Tokson

Yonathan A. Arbel et al., Open Questions in Law and AI Safety: An Emerging Research Agenda, Lawfare (2024).

unpaginated Lawfare online research-agenda essay

Frontier AI may amplify both technological benefits and society-wide risks beyond those of earlier technologies

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Opening and Thesis,” that AI can improve socioeconomic conditions while imposing risks on individuals, communities, and society. Frontier systems may develop unusually broad capabilities and correspondingly larger hazards. This is significant because the article treats AI safety as a general governance field rather than a niche product-safety issue. It connects to general-purpose technology, frontier models, societal risk, socioeconomic benefits, technology governance, and systemic harm.

The opening establishes the scale and breadth that justify a distinct research agenda.

general-purpose technology
frontier models
societal risk
socioeconomic benefits
technology governance
systemic harm

The article does not quantify net benefits, risk probabilities, or the timing of frontier capabilities.

The opening compares AI with past advances and highlights the possibility of substantially greater capabilities and risks.


Uncertainty about AI’s path increases the importance of proactive legal work rather than justifying delay

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Opening and Thesis,” that rapid and unpredictable development makes consequences difficult to specify. Legal scholars should nevertheless help address large-scale challenges in advance so society can retain benefits while mitigating harms. This is significant because epistemic uncertainty becomes a reason to build governance capacity, not a reason for paralysis. It connects to anticipatory regulation, uncertainty, institutional preparedness, risk mitigation, option value, and adaptive governance.

The proposition supplies the temporal rationale for an emerging field before its objects are fully settled.

anticipatory regulation
uncertainty
institutional preparedness
risk mitigation
option value
adaptive governance

Proactive work can still be premature or misdirected; the essay does not prescribe an immediate rule for every risk.

The authors juxtapose unpredictable development with the need for proactive legal attention.


A multidisciplinary workshop produced shared propositions and a preliminary agenda meant to widen legal participation in AI safety

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that more than twenty participants spanning law, technical fields, policy, philosophy, jurisprudence, and the humanities met in August 2023 to examine societal-scale AI risk. The publication shares their common ground and questions with a wider scholarly community. This is significant because the agenda is the output of structured interdisciplinary convening rather than a purportedly exhaustive taxonomy. It connects to workshop methodology, interdisciplinary research, agenda setting, legal scholarship, AI safety communities, and knowledge synthesis.

The method explains both the agenda’s breadth and its acknowledged incompleteness.

workshop methodology
interdisciplinary research
agenda setting
legal scholarship
AI safety communities
knowledge synthesis

The agenda reflects a small self-selected meeting and does not claim representative consensus across the legal academy or technical community.

The initiative section gives the workshop date, participant range, aim, and resulting shared propositions.


Workshop participants agreed on society-wide AI impact while disagreeing about its extent, character, and timeline

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that participants expected AI to affect human life, the economy, and society profoundly. Their consensus did not extend to how large, what kind, or how soon those effects would be. This is significant because shared governance concern does not require agreement on a single forecast. It connects to forecast uncertainty, societal transformation, economic change, expert consensus, scenario planning, and technological timelines.

The proposition defines a narrow common ground robust to disagreement about projections.

forecast uncertainty
societal transformation
economic change
expert consensus
scenario planning
technological timelines

The statement reports workshop consensus rather than a population survey or empirical forecast.

The article expressly distinguishes agreement on significant society-wide effects from debate over extent, nature, and timing.


AI safety concerns encompass large-scale threats to life, bodily integrity, and freedom through many distinct causal pathways

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that positive potential coexists with threats to human life, limb, and freedom. Possible pathways include bioterrorism, cyberattack, warfare, manipulation, mass surveillance, and totalitarian control. This is significant because AI safety is a portfolio of heterogeneous risks rather than one catastrophe narrative. It connects to bioterrorism, cybersecurity, warfare, manipulation, surveillance, and authoritarianism.

The risk taxonomy broadens legal inquiry across public, private, domestic, and international domains.

bioterrorism cybersecurity warfare manipulation surveillance authoritarianism

The list is illustrative and does not assign probabilities or establish that every pathway will materialize.

The second shared proposition defines safety concerns and enumerates multiple harm pathways.


Safety risks are likely to intensify as systems gain capability and autonomy and become embedded throughout society and the economy

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that inadequate safeguards will allow risks to grow with model capability, autonomous action, and broad integration. These three developments increase what systems can do, how independently they can do it, and how many social processes they can affect. This is significant because risk depends on deployment structure as well as raw technical performance. It connects to capability scaling, autonomy, system integration, regulatory safeguards, exposure, and systemic dependence.

The proposition identifies three multipliers of safety risk.

capability scaling
autonomy
system integration
regulatory safeguards
exposure
systemic dependence

The claim is directional and conditional; it does not provide a quantitative relationship or say that capability growth always increases net risk.

The authors tie intensifying risk to more capable, autonomous, and integrated systems absent safeguards.


Lawyers have distinctive expertise because AI alignment is a principal-agent problem shaped by incentives and collective action

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that the alignment problem is a new version of a problem familiar to law: how a principal governs an agent with imperfectly matching incentives. Legal training also attends to private incentives and failures of collective action. This is significant because AI safety can draw on established institutional analysis rather than treating alignment as exclusively technical. It connects to principal–agent theory, incentive alignment, collective-action problems, organizational law, governance, and AI alignment.

The analogy explains why legal scholars possess relevant conceptual tools.

principal–agent theory
incentive alignment
collective-action problems
organizational law
governance
AI alignment

The analogy does not imply that legal solutions alone can solve technical alignment or that software agents behave exactly like humans.

The third shared proposition explicitly identifies alignment as another principal-agent problem.


Lawyers can design rules for unpredictable systems, combine ex ante and ex post tools, and translate across specialized disciplines

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “The Legal AI Safety Initiative,” that lawyers routinely build rules for complex behavior that cannot be fully anticipated. Their exposure to preventive and remedial regulation and their capacity to communicate with technologists, economists, and social scientists make them connective tissue across domains. This is significant because effective AI safety needs institutional translation as well as technical invention. It connects to ex ante regulation, ex post liability, rule design, interdisciplinarity, regulatory translation, and institutional coordination.

The proposition specifies legal scholars’ practical contribution beyond doctrinal commentary.

ex ante regulation
ex post liability
rule design
interdisciplinarity
regulatory translation
institutional coordination

Interdisciplinary capacity varies among lawyers, and translation does not remove the need for domain-specific technical expertise.

The article lists rule design, regulatory mechanisms, and cross-disciplinary translation as distinct legal advantages.


AI safety encompasses large-scale threats to life, bodily integrity, autonomy, and the environment arising through misuse, system failure, or misaligned action

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that AI safety should encompass short- and long-term risks to life, bodily integrity, human autonomy, and the environment. They organize causal pathways into deliberate misuse or abuse, unintentional system failure, and autonomous action that is misaligned with human interests. This is significant because the definition joins familiar present-day harms to more systemic future risks without assuming that every pathway is identical. It connects to technology risk, human rights, environmental protection, product failure, malicious use, and AI alignment.

A broad but structured definition gives legal researchers a common object of inquiry.

technology risk
human rights
environmental protection
product failure
malicious use
AI alignment

The article offers an organizing definition, not a probability estimate or an exhaustive taxonomy of AI harms.

The section defines the interests at stake and expressly names misuse, failure, and misalignment as distinct pathways.


Dual-use language and biological models may lower barriers to developing explosives, chemical weapons, or dangerous pathogens

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that increasingly capable models can supply knowledge and workflows useful for harmful biological or chemical activity. The essay points to assistance involving explosives, chemical weapons, protein design, and development of a pandemic-capable virus as examples of dual-use risk. This is significant because general-purpose assistance can alter who is able to undertake dangerous projects and how quickly. It connects to biosecurity, chemical-weapons control, dual-use research, capability diffusion, pandemic preparedness, and information hazards.

The examples show how a generally useful model may increase the operational capacity of malicious actors.

biosecurity
chemical-weapons control
dual-use research
capability diffusion
pandemic preparedness
information hazards

The essay identifies plausible capabilities and risk pathways; it does not establish that existing models can independently execute every listed attack.

The article specifically describes weapon guidance, protein design, and virus-development assistance as potential dual-use applications.


AI coding capabilities can amplify cyberattacks and expose critical infrastructure to large-scale disruption

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that models able to write and analyze code can make cyber operations more powerful and accessible. Because networked systems support critical infrastructure, those capabilities can propagate harm well beyond the immediate software target. This is significant because AI safety law must account for scalable attacks on interdependent public and private systems, not only failures inside a model. It connects to cybersecurity, critical infrastructure, automated vulnerability discovery, cybercrime, systemic risk, and national resilience.

The cyber pathway demonstrates how model capability can translate into distributed physical and economic consequences.

cybersecurity
critical infrastructure
automated vulnerability discovery
cybercrime
systemic risk
national resilience

The essay frames an exposure and research problem rather than quantifying the marginal cyber risk caused by any particular model.

The article links AI coding capacity to cyberattacks and the vulnerability of critical infrastructure.


Commercial pressure to automate labor will place AI agents in digital and physical environments where both intended goals and unexpected conduct can cause social harm

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that the commercial value of automating human labor will encourage deployment of increasingly agentic systems in digital and physical spaces. Harm may follow because designers choose goals that benefit themselves while externalizing costs, or because an agent behaves in ways even its designers did not foresee. This is significant because the risk is simultaneously an incentives problem and a control problem. It connects to labor automation, externalities, autonomous agents, corporate incentives, accident law, and alignment failures.

Separating principal-serving design from unintended behavior clarifies two different legal targets for intervention.

labor automation
externalities
autonomous agents
corporate incentives
accident law
alignment failures

The prediction concerns deployment incentives and possible pathways; it does not claim that all commercial agents will become dangerous or uncontrollable.

The article describes both socially harmful designer objectives and unexpected catastrophic behavior by deployed agents.


General capability, autonomy, tool access, self-improvement, and opacity can compound AI risk while making auditing more difficult

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that several system properties may magnify one another: broad competence, autonomous operation, access to external tools, potential self-improvement, and limited human understanding of internal reasoning. A capable system able to act and acquire resources may pose a different regulatory problem from a narrow tool, especially when reliable audit is difficult. This is significant because safety cannot be inferred from a single benchmark or feature in isolation. It connects to general-purpose AI, agentic autonomy, tool use, recursive improvement, interpretability, and model auditing.

The compound-risk account directs regulators toward interacting capabilities and observability rather than model size alone.

general-purpose AI
agentic autonomy
tool use
recursive improvement
interpretability
model auditing

The essay flags these properties as risk factors and open concerns; it does not specify a universal threshold at which they become unacceptable.

The source enumerates increasing performance, autonomy, tools, self-improvement, and opacity as relevant safety concerns.


AI risk is driven not only by model properties but also by markets, geopolitics, industry structure, oversight failures, politics, public knowledge, and the supply of safety research

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that the surrounding institutional environment can amplify or constrain technical risk. Competitive markets, interstate rivalry, concentrated industry structure, self-governance, deficient oversight, political consequences, limited public understanding, and underinvestment in safety research all matter. This is significant because changing a model’s design is only one route to safety; law also shapes the incentives and institutions around development and deployment. It connects to political economy, geopolitical competition, industrial organization, regulatory capacity, public risk communication, and research funding.

The institutional account broadens AI safety from engineering controls to the governance of the development ecosystem.

political economy
geopolitical competition
industrial organization
regulatory capacity
public risk communication
research funding

The article identifies candidate external drivers without ranking their relative causal importance or prescribing a single institutional design.

The essay lists market, geopolitical, organizational, oversight, political, informational, and research conditions as sources of risk.


AI safety presents legal questions across fields, and lawyers need not master advanced mathematics or engineering before contributing

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that consequential AI-safety questions arise throughout public and private law. Lawyers of many specialties can contribute their existing doctrinal and institutional expertise without first becoming mathematicians or engineers, provided that they engage responsibly with the technology and relevant experts. This is significant because a perceived technical barrier could otherwise keep legal scholarship out of a fast-forming governance field. It connects to interdisciplinary research, legal education, technology governance, doctrinal expertise, professional specialization, and public-interest law.

The invitation enlarges the pool of scholars able to examine how existing legal institutions bear on AI risk.

interdisciplinary research
legal education
technology governance
doctrinal expertise
professional specialization
public-interest law

Technical literacy and collaboration remain important; the claim rejects an advanced-technical prerequisite, not the need to understand the systems under study.

The authors expressly invite lawyers of all stripes and state that advanced math or engineering knowledge is not required.


Institutional collaboration can lower entry costs and organize legal research on AI safety

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that organized collaboration can help lawyers enter and shape the AI-safety field. They identify the Center for Law and AI Risk and its collaboration with the Center for AI Safety as sources of programming and resources for interested researchers. This is significant because an agenda becomes more useful when institutions can convene expertise, circulate problems, and support work across disciplinary boundaries. It connects to research networks, institutional capacity, knowledge infrastructure, interdisciplinary convening, agenda setting, and AI governance scholarship.

The institutional invitation turns the essay from a static question list into a platform for a continuing research community.

research networks
institutional capacity
knowledge infrastructure
interdisciplinary convening
agenda setting
AI governance scholarship

The existence of sponsoring organizations does not validate any particular policy answer or make the agenda representative of all relevant communities.

The article names CLAIR and the Center for AI Safety and offers resources and collaboration to legal researchers.


The question list is a preliminary and nonexhaustive research agenda meant to evolve rather than a settled policy program

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “AI Safety and the Law,” that their inventory is deliberately preliminary, nonexhaustive, and open to periodic revision. It emerged from a small group and a single meeting, so the authors invite additions and criticism rather than presenting consensus answers. This is significant because the epistemic status of the article is part of its substantive meaning: it maps problems without pretending to resolve them. It connects to research agendas, epistemic humility, participatory governance, scholarly iteration, issue spotting, and policy uncertainty.

The disclaimer prevents the agenda’s questions from being misread as endorsed solutions or a comprehensive field map.

research agendas
epistemic humility
participatory governance
scholarly iteration
issue spotting
policy uncertainty

The agenda’s small-group origin and nonexhaustive design mean omitted perspectives and questions should not be treated as unimportant.

The authors expressly describe the list as preliminary and nonexhaustive, disclose its workshop origin, and promise periodic updates.


AI regulation first requires defensible choices about the regulable unit and the organizational level at which safety duties attach

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Key Background Questions,” that legal research must determine what counts as an AI system for regulatory purposes and whether intervention should focus on engineers, firms, or technical systems. Those framing choices determine who can comply, who can be monitored, and which conduct a rule reaches. This is significant because an imprecise object or level of regulation can make even a sensible safety norm difficult to administer. It connects to legal classification, regulatory targets, organizational responsibility, system boundaries, compliance design, and enforcement architecture.

Defining the object and addressee of regulation is logically prior to selecting substantive obligations.

legal classification
regulatory targets
organizational responsibility
system boundaries
compliance design
enforcement architecture

The agenda poses these classification questions and does not choose a single definition or level of governance.

The first background questions ask what system or unit should be regulated and whether rules should operate at engineer, company, or system level.


A safety regime must investigate when intervention is justified, whether some technologies should be banned, and where along the AI supply chain rules should operate

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Key Background Questions,” that regulatory design depends on timing, intensity, and location. The agenda asks when an AI system becomes dangerous enough to regulate, whether exceptionally dangerous technologies should be prohibited, and whether rules belong upstream in model creation, midstream in distribution, or downstream in use. This is significant because identical risks may call for different tools depending on when and where control is feasible. It connects to precautionary regulation, technology bans, supply-chain governance, upstream controls, downstream liability, and regulatory timing.

The questions expose tradeoffs between early prevention, adaptable distribution controls, and use-specific enforcement.

precautionary regulation
technology bans
supply-chain governance
upstream controls
downstream liability
regulatory timing

The authors identify the design dimensions but do not endorse a particular danger threshold, ban, or point of intervention.

The agenda directly asks when to regulate, whether to ban dangerous technology, and how to choose among upstream, midstream, and downstream regulation.


Foundational AI governance must address geopolitical enforcement, deceptive or inscrutable systems, regulatory obsolescence, and threshold mechanisms such as disclosure, registration, or licensing

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Key Background Questions,” that an effective safety program cannot assume cooperative firms, transparent models, stable technology, or purely domestic conditions. The agenda therefore asks how to enforce rules amid geopolitical competition, audit systems that may be opaque or deceptive, keep law current as capabilities change, and decide when disclosure, registration, or licensing is warranted. This is significant because governance mechanisms fail if their information and jurisdictional assumptions are unrealistic. It connects to international enforcement, adversarial auditing, regulatory agility, disclosure duties, registration systems, and licensing regimes.

The cluster identifies institutional preconditions for monitoring and enforcing substantive safety rules.

international enforcement
adversarial auditing
regulatory agility
disclosure duties
registration systems
licensing regimes

The agenda poses rather than resolves these questions and does not claim that licensing or registration is appropriate for every system.

The remaining background questions cover geopolitics, inscrutability and deceit, technological pace, and disclosure, registration, or licensing.


International AI-safety research must compare regulatory blueprints, combinations of hard and soft law, and institutions capable of slowing dangerous competitive races

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “International Law,” that AI safety requires investigation of international regulatory models, the proper mixture of binding and nonbinding norms, and mechanisms for restraining races that reward speed over caution. The agenda asks whether existing or new coalitions and institutions—including structures associated with NATO, the United Nations, or the World Trade Organization—can coordinate action. This is significant because nationally stringent rules may be undermined by cross-border development and strategic competition. It connects to treaty design, soft law, multilateral institutions, regulatory competition, coalition governance, and race dynamics.

The questions locate domestic AI policy inside an interdependent international system where unilateral safeguards may be unstable.

treaty design
soft law
multilateral institutions
regulatory competition
coalition governance
race dynamics

The authors do not endorse a specific organization, treaty, or global regulator; they identify options for comparative inquiry.

The international-law list asks about blueprints, hard and soft norms, race slowing, coalitions, and NATO, U.N., or WTO roles.


AI-safety governance must investigate how human rights apply and whether global norms can legitimately accommodate cultural variation

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “International Law,” that international governance raises normative as well as institutional questions. The agenda asks how human-rights law should apply to AI safety and whether global rules should enforce common values or leave room for culturally relative conceptions of risk, autonomy, and acceptable control. This is significant because worldwide coordination can protect people while also entrenching contested value judgments. It connects to international human rights, cultural pluralism, universalism, global administrative law, democratic legitimacy, and value alignment.

The inquiry makes legitimacy and value conflict central to international safety design rather than treating coordination as purely technical.

international human rights
cultural pluralism
universalism
global administrative law
democratic legitimacy
value alignment

The essay asks how to reconcile global norms and cultural difference; it does not supply a universal hierarchy of values.

The final international questions concern human-rights norms and the tension between culturally relative and global rules.


National-security law must govern AI-enabled weapons across creation and deployment, including human control, proportionality, humanitarian law, and fitness for use

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “National Security Law,” that AI changes legal questions throughout the weapons lifecycle. The agenda asks how law should govern creation and deployment, when lethal systems require a human in the loop, whether proportionality and international humanitarian law can be built into system operation, and how to determine a weapon’s fitness for purpose. This is significant because safety obligations must function under battlefield uncertainty and delegated machine action. It connects to autonomous weapons, meaningful human control, proportionality, international humanitarian law, weapons review, and military procurement.

The questions connect system design and deployment doctrine to established limits on the use of force.

autonomous weapons
meaningful human control
proportionality
international humanitarian law
weapons review
military procurement

The agenda does not answer when human control is legally sufficient or assert that proportionality can in fact be fully encoded.

The national-security questions expressly cover arms creation and deployment, human-in-the-loop requirements, IHL, proportionality, and fitness.


AI national-security governance must reconcile secrecy and oversight while clarifying sovereignty, attack attribution, export controls, influence operations, and sensitive data

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “National Security Law,” that military and intelligence uses create a dense cluster of governance problems beyond weapons performance. The agenda asks how to balance secrecy against oversight, determine when AI conduct violates sovereignty or constitutes an act of war, impute machine conduct to states, design export controls, and govern offensive influence operations and data access. This is significant because attribution, jurisdiction, and information control shape whether substantive limits can be enforced. It connects to state responsibility, sovereignty, intelligence oversight, export control, information warfare, and data governance.

The questions show that AI safety in national security depends on accountability and cross-border control as much as technical reliability.

state responsibility
sovereignty
intelligence oversight
export control
information warfare
data governance

The authors pose these issues as an agenda and do not resolve thresholds for attribution, armed attack, secrecy, or controlled exports.

The national-security list includes secrecy, sovereignty, acts of war, imputation, export controls, influence operations, and data collection.


American AI-safety regulation must be designed within institutional and constitutional limits involving agency authority, federalism, property, and speech

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Constitutional and Administrative Law,” that federal tools and agency expertise cannot be evaluated apart from limits on congressional delegation and administrative power. The agenda also asks when state regulation burdens interstate commerce, whether an emergency shutdown could produce a taking, how federalism should divide responsibility, and when safety rules implicate the First Amendment. This is significant because powerful interventions may be ineffective if they lack durable constitutional authority. It connects to administrative agencies, nondelegation, dormant Commerce Clause, regulatory takings, federalism, and freedom of speech.

The inquiry makes legal durability and allocation of public power part of safety policy design.

administrative agencies
nondelegation
dormant Commerce Clause
regulatory takings
federalism
freedom of speech

The agenda identifies possible constraints without concluding that any specified AI rule is unconstitutional or preempted.

The constitutional and administrative list covers federal authority, expertise, congressional limits, state regulation, shutdowns and takings, federalism, and speech.


AI governance must reconcile regulatory agility with the major questions doctrine and compare centralized, decentralized, and redundant safety architectures

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Constitutional and Administrative Law,” that rapidly changing technology creates pressure for flexible expert governance, while the major questions doctrine may demand clearer legislative authorization for consequential agency action. The agenda separately asks whether safety should be centralized, decentralized, or organized as overlapping “Swiss cheese” layers whose imperfections do not align. This is significant because institutional architecture determines both adaptability and resilience. It connects to the major questions doctrine, regulatory agility, agency delegation, polycentric governance, defense in depth, and institutional redundancy.

The paired questions expose a tradeoff between fast adaptation, legal authorization, and failure-resistant distribution of responsibility.

major questions doctrine
regulatory agility
agency delegation
polycentric governance
defense in depth
institutional redundancy

The article poses rather than resolves the institutional choice and does not establish that more layers always produce greater safety.

The section asks how to preserve agility after major-questions decisions and compares centralized, decentralized, and Swiss-cheese models.


Antitrust analysis must distinguish safety-enhancing coordination and concentration from collusion, exclusion, entry barriers, and the risks of centralized AI infrastructure

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Antitrust Law,” that competition law may both obstruct and support AI safety. The agenda asks when firms may coordinate or pause development, whether concentration makes oversight easier or creates dangerous control, how centralization across the technology stack affects safety, and whether safety requirements become barriers to entry that protect incumbents. This is significant because interventions advertised as protective can also reduce competition and distribute power unevenly. It connects to collusion doctrine, market concentration, technology stacks, barriers to entry, regulatory capture, and cooperative standard setting.

The questions prevent safety and competition from being treated as automatically aligned or inherently opposed.

collusion doctrine
market concentration
technology stacks
barriers to entry
regulatory capture
cooperative standard setting

The agenda does not decide whether a concentrated or competitive market is safer in general; effects depend on institutional and technical conditions.

The antitrust list addresses coordination and pauses, concentration, stack centralization, safety regulation, and competitive barriers.


Private law must adapt contracts, agency, organizational forms, and liability to reward hacking, autonomous agents, large-scale harms, and injuries without an obvious human victim

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Private Law,” that familiar doctrines may need to govern unfamiliar AI relationships. The agenda links incomplete contracting and principal–agent theory to reward hacking and misspecified objectives; asks how agents should contract or participate in corporations; and examines ex post liability for large-scale harms, harms to users, harms without a human victim, and possible direct liability for AI systems. This is significant because private law allocates incentives and losses after deployment even when public regulation is incomplete. It connects to incomplete contracts, agency law, corporate personhood, tort liability, legal standing, and autonomous transactions.

The private-law questions join alignment problems to established mechanisms for delegation, organization, compensation, and deterrence.

incomplete contracts
agency law
corporate personhood
tort liability
legal standing
autonomous transactions

The authors identify problems rather than endorsing AI personhood, direct machine liability, or a specific liability standard.

The private-law list covers reward hacking, agent contracts and corporations, ex post liability, different victim classes, and direct AI liability.


Criminal law must clarify responsibility among laboratories, users, and AI agents while assessing new offenses, sanctions, and investigative limits

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Criminal Law,” that AI-related wrongdoing destabilizes ordinary assumptions about actors and culpability. The agenda asks how criminal responsibility should be distributed among model laboratories, human users, and AI agents; whether new crimes are needed; whether criminal penalties are an effective safety instrument; and how AI affects investigative powers and constraints. This is significant because deterrence depends on identifying an accountable actor with the requisite conduct and mental state. It connects to mens rea, accomplice liability, corporate crime, cybercrime, criminal deterrence, and investigative procedure.

The questions reveal a potential gap between machine-mediated harm and criminal law’s human-centered attribution rules.

mens rea
accomplice liability
corporate crime
cybercrime
criminal deterrence
investigative procedure

The agenda does not assert that an AI can presently bear criminal responsibility or that new crimes are necessarily preferable to existing law.

The criminal-law list identifies possible liability for labs, users, and agents, along with new crimes, sanctions, and investigation.


Tax policy could shape the pace, distribution, and internalization of AI risk, but its targets and incidence require careful study

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Tax Law,” that fiscal instruments may create safety incentives, slow development, distribute AI’s gains and losses more equitably, or make developers and users bear costs they otherwise externalize. The agenda asks whether taxes should fall on laboratories, licensors, licensees, or other actors and who would ultimately bear them. This is significant because a formally imposed tax may alter behavior—or shift burdens—in ways different from its nominal design. It connects to Pigouvian taxation, tax incidence, innovation policy, distributive justice, externalities, and technology pacing.

The questions treat taxation as a behavioral and distributive safety tool rather than solely a revenue measure.

Pigouvian taxation
tax incidence
innovation policy
distributive justice
externalities
technology pacing

The article poses these possibilities without establishing an optimal tax base, rate, target, or effect on the pace of innovation.

The tax questions concern incentives, a pace tax, equitable distribution, tax incidence among market actors, and internalization of unsafe costs.


AI safety research must address environmental costs and injuries alongside capture, self-regulation, market governance, political trust, and changes to governing institutions

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Environmental Law” and “Political Economy,” that the agenda extends from the physical burdens of training and operating models to AI-directed or autonomous environmental injury. It also asks how AI will reshape governance, invite regulatory capture, perform under self-regulation or market discipline, and affect political trust. This is significant because safety includes ecological and institutional conditions, not only immediate injury to individual users. It connects to energy and water use, environmental liability, regulatory capture, industry self-governance, market discipline, and democratic trust.

Combining these domains reveals that AI can change both material resources and the institutions charged with governing those effects.

energy and water use
environmental liability
regulatory capture
industry self-governance
market discipline
democratic trust

The authors pose distinct environmental and political-economy questions; this synthesis does not claim that they share one causal mechanism or settled remedy.

The source asks about training harms and autonomous environmental liability, then governance effects, capture, self-regulation, markets, and political trust.


Intellectual Property Law and Corporate and Finance Law

Innovation and corporate law should be studied as safety levers through patents, trade secrecy, alternative rewards, board oversight, executive incentives, limited liability, and agent transactions

Professor Yonathan A. Arbel and his coauthors claim, in “Open Questions in Law and AI Safety: An Emerging Research Agenda,” an unpaginated Lawfare essay, under “Intellectual Property Law” and “Corporate and Finance Law,” that background rules governing invention and enterprise can change safety incentives. The agenda asks whether patents or alternatives should reward positive safety externalities, when trade secrecy obstructs scrutiny, how boards and compensation should promote oversight, whether limited liability externalizes catastrophic losses, and how autonomous-agent transactions require disclosure or compensation. This is significant because much AI risk is produced inside firms before public enforcement begins. It connects to patent incentives, trade secrets, corporate governance, executive compensation, limited liability, and financial disclosure.

The final domains locate safety responsibilities in the legal infrastructure that finances, rewards, conceals, and supervises AI development.

patent incentives
trade secrets
corporate governance
executive compensation
limited liability
financial disclosure

The agenda does not endorse abolishing trade secrecy or limited liability, granting patents, or recognizing autonomous agents as legal persons.

The IP and corporate-finance questions cover safety innovation, secrecy, alternative incentives, governance, compensation, liability, agent transactions, and disclosure.