draft enforceable regulatory text

To address challenges in legal compliance checking—including high subjectivity in regulatory interpretation, dynamic evolution of legislation, and difficulties in cross-disciplinary collaboration—this paper introduces eFLINT, a domain-specific language for computable modeling and automated verification of legal rules, regulatory requirements, and contractual clauses. eFLINT integrates declarative and procedural paradigms, explicitly linking legal concepts to executable computational logic. It combines formal specification, context-aware reasoning, and scenario-based modeling to enable dynamic, end-to-end compliance verification across system design, runtime, and post-execution phases. Designed to balance expressiveness and executability, eFLINT reconciles conflicting requirements through principled language design. Drawing on multi-scenario industrial deployments, the paper distills actionable design principles and a methodology for automation-oriented compliance languages. It contributes both a reusable technical framework and theoretical foundations for computable regulation research in legal technology.

Natural language often introduces execution ambiguity in computational legal applications, whereas formal languages risk undermining legal legitimacy and public accessibility. This tension raises fundamental trade-offs among normative clarity, public comprehensibility, and algorithmic executability. Method: Drawing on an EU road transport regulation case study, the paper conducts a comparative analysis of natural-language legal texts and formal computational models, integrating jurisprudential reasoning, normative linguistics, and computational logic evaluation. Contribution/Results: The study systematically identifies the inherent tensions between natural and formal languages in representing core legal principles—particularly interpretability, traceability, and intelligibility—and proposes design principles for hybrid normative frameworks that simultaneously preserve legal integrity and ensure machine operability. It is the first work to rigorously characterize the tripartite trade-off across normative precision, democratic accessibility, and computational enforceability, offering a foundational methodology for legally sound legal informatics.

Legal compliance of machine learning models cannot be directly encoded; instead, abstract legal obligations must be “indirectly operationalized” into verifiable model design choices. Existing approaches either focus narrowly on software-level compliance or overlook legal complexity, failing to address two core challenges: the multiplicity of legal interpretations and the unpredictability of performance–compliance trade-offs. Method: We propose a five-stage interdisciplinary framework introducing the first legal–ML co-modeling paradigm, embedding legal reasoning throughout the ML development lifecycle. It features a legally adaptable operationalization mechanism and a multi-objective trade-off evaluation system. Contribution/Results: Evaluated in an anti-money laundering use case, the framework identifies an optimal configuration achieving both high detection accuracy (12% F1-score improvement) and legal defensibility, demonstrating its systematic capacity to jointly optimize predictive performance and legal legitimacy.

This study addresses the absence of concrete mapping mechanisms for implementing the EU AI Act within agile teams. Employing a Design Science Research methodology, this work proposes a novel framework that translates abstract regulatory requirements into actionable agile compliance guidelines. Through a traffic light taxonomy and expert interviews, an action catalog comprising twelve practices covering roles and risk management was constructed. The results demonstrate that these guidelines are both comprehensible and relevant, establishing that compliance should be integrated into existing agile activities rather than treated as a parallel process. Ultimately, this research bridges the gap in regulatory operationalization, providing a reusable methodological foundation that enables agile teams to achieve compliance without compromising iterative efficiency.

To address the inefficiency and error-proneness of manual regulatory compliance checking, this paper proposes an OWL DL formalization method for natural language specifications. The method introduces a novel structured text annotation scheme and employs a rule-driven deterministic transformation algorithm to automatically map specification texts to OWL DL ontologies. It further integrates Protégé with the HermiT reasoner to enable machine-readable semantic representation and automated compliance verification. A proof-of-concept evaluation in the construction domain demonstrates successful translation of multiple natural language regulations into OWL DL ontologies and accurate identification of compliant and non-compliant scenarios. This work bridges a critical gap between regulatory semantic modeling and automated reasoning, delivering a scalable, methodology-driven foundation for automating compliance checking.

This work addresses the unpredictable interpretive choices often implicit in large language model (LLM) formalizations of legal provisions, which undermine the comparability and explainability of reasoning outcomes. The authors propose a systematic approach that integrates graph node matching with SAT solvers to enumerate divergent inferences arising from alternative formalizations when applied to identical legal cases. These divergences are then rendered into natural-language scenarios amenable to expert legal review. For the first time, this method maps formalization discrepancies onto intelligible edge cases, revealing their qualitative connection to real-world legal disputes. Experiments on ten EU legal provisions demonstrate that structural similarity among formalizations correlates poorly with behavioral agreement, whereas the generated divergence cases effectively capture actual conflicts in legal interpretation.

This work addresses the lack of a rigorous semantic foundation and type safety guarantees in formal verification of smart contracts by presenting, for the first time, a complete operational semantics and type system for the Act language, together with a corresponding metatheoretic framework. Through formal semantic modeling and a proof of type safety, we rigorously establish that the Act language satisfies type safety properties. This contribution not only fills a critical gap in the theoretical underpinnings of Act but also provides a solid semantic basis and strong safety assurances for reliable, automated verification of smart contracts.

本文提出了一种通过双向反转验证的条款级模型和构建协议，以解决法律交叉引用中的准确性和一致性问题，应用于欧盟AI法案相关法规。

This work addresses the error-prone and labor-intensive process of manually translating regulatory texts such as the GDPR and the EU AI Act into actionable software requirements. The authors propose Reg2Req, the first end-to-end automated pipeline that leverages natural language processing to identify regulatory provisions, generate system-agnostic software requirements accompanied by plain-language explanations, and establish traceability links. The approach supports requirement classification, use case seed generation, and cross-reference analysis, achieving macro-averaged F1 scores of 0.82 on the GDPR and 0.78 on the EU AI Act. A user study demonstrates that the generated plain-language explanations significantly enhance users’ comprehension and confidence in taking compliance actions (p < 0.001), with all participants expressing willingness to adopt the output as a starting point for compliance efforts.

本文提出一种模型，通过设计科学研究方法解决在软件工程中选择大型语言模型时面临的治理与合规难题，采用多层结构和评估协议以增强决策过程中的合规性。