arXiv subjects

Nicholas Caputo

Publications and source records attributed to Nicholas Caputo.

5 recordsLinked to original sources

arXiv2026

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.

cs.CY↗

arXiv2026

Alignment as Jurisprudence

Jurisprudence, the study of how judges should properly decide cases, and alignment, the science of getting AI models to conform to human values, share a fundamental structure. These seemingly distant fields both seek to predict and shape how decisions by powerful actors, in one case judges and in the other increasingly powerful artificial intelligences, will be made in the unknown future. And they use similar tools of the specification and interpretation of language to try to accomplish those goals. The great debates of jurisprudence, about what the law is and what it should be, can provide insight into alignment, and lessons from what does and does not work in alignment can help make progress in jurisprudence. This essay puts the two fields directly into conversation. Drawing on leading accounts of jurisprudence, particularly Dworkin's principle-oriented interpretivism and Sunstein's positivist account of law as analogical reasoning, and on cutting-edge alignment approaches, namely Constitutional AI and case-based reasoning, it illustrates the value of a more sophisticated legally-inspired approach to the interplay of rules and cases in finetuning alignment and points to ways that AI can provide a better understanding of how the law works and how it can be improved by the introduction of AI. AI systems and the law should operate to empower people to act in the world, helping to expand their capabilities and the extent to which they are able to achieve their goals. As AI continues to improve in capacity, and as the constraints that legal theory places on human judges seem be coming undone, the conversation between these two fields will become increasingly essential and may help point to a better version of both.

cs.AI↗

arXiv2026

Administrative Law's Fourth Settlement: AI and the Scrutable State

Since 1887, administrative law has confronted a problem of institutional cognition. Expert agencies are needed to govern technologically complex systems, but expertise makes agency decisions difficult for courts, Congress, and the public to understand and oversee. Administrative law has responded to this "capability-accountability trap" by requiring records, reason-giving, and transparency, drawn together through procedural review. These devices have preserved legality but have piled up, making government both less comprehensible and less effective. This Article offers a new account of the Supreme Court's recent administrative law retrenchment, rooted in problems of institutional structure and information-processing. From Loper Bright through Trump v. Slaughter, the Court has reallocated authority to entities it regards as comprehensible and attributable. It is attempting to restore accountability by making government "scrutable," comprehensible to its overseers and the public, but in doing so it is sacrificing capability and undermining the effectiveness of administration. AI offers a different path. Deployed correctly, AI could help make government both more effective and more transparent, translating technical complexity into accessible terms, surfacing assumptions, and enabling substantive verification of agency reasoning. This technical integration must be accompanied by updated administrative law, built around a Model and System Dossier that extends the administrative record to AI decision-making; a material-model-change trigger specifying when AI updates require new process; and a deference to audit standard that rewards agencies for auditable evaluation of AI uses. The result a "Fourth Settlement," administrative law that escapes the capability-accountability trap by preserving capability while restoring comprehensible oversight of administration.

cs.CY↗

arXiv2026

Legal Alignment for Safe and Ethical AI

Alignment of artificial intelligence (AI) encompasses the normative problem of specifying how AI systems should act and the technical problem of ensuring AI systems comply with those specifications. To date, AI alignment has generally overlooked an important source of knowledge and practice for grappling with these problems: law. In this paper, we survey the emerging field of legal alignment that aims to fill this gap and systematize research that studies how legal rules, principles, and methods can be leveraged to address problems of alignment and inform the design of AI systems that operate safely and ethically. Our survey provides a taxonomy of the three core research pathways of legal alignment and explores how each can be operationalized in practice: (1) designing AI systems to comply with the content of legal rules developed through legitimate institutions and processes, (2) adapting methods from legal interpretation to guide how AI systems reason and make decisions, and (3) harnessing legal concepts as a structural blueprint for confronting challenges of reliability, trust, and cooperation in AI systems. These research pathways present new conceptual, empirical, and institutional questions, which include examining the specific set of laws that particular AI systems should follow, creating evaluations to assess their legal compliance in real-world settings, and developing governance frameworks to support the implementation of legal alignment in practice. Tackling these questions requires expertise across law, computer science, and other disciplines, offering these communities the opportunity to collaborate in designing AI for the better.

cs.CY↗

arXiv2025

(When) Should We Delegate AI Governance to AIs? Some Lessons from Administrative Law

Advanced AI systems are now being used in AI governance. Practitioners will likely delegate an increasing number of tasks to them as they improve and governance becomes harder. However, using AI for governance risks serious harms because human practitioners may not be able to understand AI decisions or determine whether they are aligned to the user's interests. Delegation may also undermine governance's legitimacy. This paper begins to develop a principled framework for when to delegate AI governance to AIs and when (and how) to maintain human participation. Administrative law, which governs agencies that are (1) more expert in their domains than the legislatures that create them and the courts that oversee them and (2) potentially misaligned to their original goals, offers useful lessons. Administrative law doctrine provides examples of clear, articulated rules for when delegation can occur, what delegation can consist of, and what processes can keep agencies aligned even as they are empowered to achieve their goals. The lessons of administrative law provide a foundation for how AI governance can use AI in a safe, accountable, and effective way.

cs.CY↗