legal

Privacy laws designate “consent” as a lawful basis for data processing, yet its translation into software implementations has long suffered from a legal–technical gap and opaque development practices. This paper proposes the first LLM-based, three-step automated framework: (1) legal clause parsing, (2) use-case compliance classification, and (3) technical requirement reconstruction—augmented by human-in-the-loop verification to ensure legal alignment. It establishes the first systematic, end-to-end mapping from privacy regulation text to executable technical specifications, enabling compliance-aware requirements engineering and use-case remediation. Empirical evaluation demonstrates that the LLM effectively identifies and rectifies non-compliant use cases, validating its feasibility for automated compliance tasks; it also reveals persistent limitations in complex legal reasoning. The work introduces a novel paradigm and practical pathway for AI-augmented legal technologization.

This study addresses the AI literacy compliance requirements imposed by the EU AI Act on deployers and providers of legal AI systems, focusing on the risk–benefit trade-off among bias mitigation, operational efficiency enhancement, and accountability strengthening. Method: It introduces the novel concept of “legal AI systems,” develops an organizational-level AI Literacy (AI-L) framework, and designs a structured assessment questionnaire roadmap integrating three dimensions: risk, benefit, and stakeholder engagement. The approach combines legal technology, AI governance, human–AI collaboration evaluation, and regulatory compliance analysis through interdisciplinary conceptual modeling and rigorous questionnaire engineering. Contribution/Results: The study delivers a practical, implementable AI literacy assessment toolkit that demonstrably enhances transparency, trustworthiness, and societal acceptability of legal AI systems—thereby enabling organizations to achieve synergistic alignment across regulatory compliance, ethical responsibility, and operational effectiveness.

This paper addresses the regulatory compliance challenges faced by edge AI systems under frameworks such as the EU Artificial Intelligence Act, with a focus on dataset compliance as a critical bottleneck. Methodologically, it integrates regulatory mapping analysis, edge-computing–constrained modeling, data governance assessment, and AI lifecycle auditing to systematically identify prevalent compliance barriers across development, deployment, and operation phases. Its primary contributions are threefold: (1) the first end-to-end legal compliance framework specifically designed for edge AI; (2) the formal establishment of dataset compliance as the technical and ethical foundation for trustworthy, transparent, and explainable AI; and (3) the deep integration of ethical requirements into technical implementation pathways. The resulting artifact is a practical, actionable best-practice guideline that enables responsible deployment of embedded AI systems and facilitates regulatory alignment and collaboration.

This study addresses the compliance challenges faced by AI agents operating within the European Union’s complex, multi-regulatory environment, particularly those arising from behavior drift and insufficient transparency across multi-agent linkages. It presents the first systematic integration of key regulatory and policy instruments—including the Artificial Intelligence Act, the Cyber Resilience Act (CRA), Standardization Request M/613, and the GPAI Code of Conduct—into a unified compliance framework tailored for AI agents. By employing regulatory mapping, a behavioral taxonomy, and data flow tracing, the work establishes correspondences between nine deployment scenarios and relevant legal triggers, and proposes a twelve-step implementation pathway. The research underscores that high-risk AI agents exhibiting untraceable behavior drift cannot satisfy the core requirements of the AI Act, necessitating providers to comprehensively audit their agents’ external behaviors, data flows, interconnected systems, and impacted entities.

Facing challenges posed by rapid AI technological evolution, regulatory uncertainty, and difficulties in cross-level coordination under the EU AI Act, this study introduces the “AI Regulatory Learning Space” — the first systematic theoretical framework bridging the gap between technical regulation and sectoral enforcement. Methodologically, it integrates RegTech modeling, multi-stakeholder collaborative learning, policy pathway analysis, and adaptive open-data governance. Contributions include: (1) a novel, mapping-capable, and operationally deployable regulatory learning space tool; (2) a dynamic implementation and adaptive governance roadmap for EU Member States; and (3) a paradigm shift in AI governance—from principle-based approaches toward reproducible, accountable, and standardized risk quantification—thereby strengthening the empirical foundations of fairness, transparency, and cross-rights coordination.

This work addresses a longstanding gap in AI alignment research—the underutilization of law as a critical source of normative and technical constraints. It introduces a novel “legal alignment” paradigm that systematically integrates legal rules, interpretive methodologies, and institutional structures into AI development. The framework advances three core directions: formal modeling of legal norms, reasoning mechanisms grounded in legal interpretation, and compliance-oriented evaluation and governance architectures. By deeply embedding legal knowledge systems into the foundations of AI alignment, this study provides both theoretical grounding and technical pathways for building lawful, trustworthy, and collaboratively capable AI systems. Furthermore, it fosters interdisciplinary synergy between legal scholarship and artificial intelligence, enabling the institutional implementation of legal alignment in real-world applications.

This study identifies systemic deficiencies in current information sources for supporting developers in privacy-sensitive software development: developers普遍 lack legal expertise, while personal experience, online resources, and AI assistants fail to deliver precise, context-aware, and actionable privacy compliance guidance. Through the first controlled comparative study—employing scenario-based simulations, think-aloud protocols, and in-depth interviews—we conducted thematic analysis of developer decision-making across these three information sources. Results reveal that experiential knowledge is constrained by domain-specific blind spots; online content is overly verbose and difficult to interpret; and AI-generated responses lack contextual grounding and problem specificity. The study articulates design requirements for “context-aware privacy support tools,” emphasizing actionability, comprehensibility, and task alignment. These findings provide empirical grounding and methodological insights for developing privacy engineering assistance systems tailored to software developers. (149 words)

Addressing the challenges of regulatory compliance verification and low interpretability in AI-driven cross-border data transfers—particularly under privacy laws such as Japan’s Act on the Protection of Personal Information (APPI)—this paper proposes the first multi-agent legal verification framework tailored to APPI Article 16. The framework decomposes compliance assessment into three specialized agents: statutory interpretation, business-context modeling, and risk adjudication, coordinated via a structured synthetic protocol for joint reasoning. Our method integrates statutory semantic parsing, context-sensitive modeling, and hierarchical risk evaluation. Evaluated on 200 amended real-world cases, it achieves an overall accuracy of 72%—a 21-percentage-point improvement over single-agent baselines—with 90% accuracy in identifying explicitly compliant cases and 100% recall in detecting violations. This work significantly enhances the verifiability and accountability of AI systems operating in dynamic, jurisdictionally heterogeneous legal environments.

The EU AI Act faces challenges including the absence of systematic methodologies for legal compliance verification, heterogeneous national preparedness, and ambiguous regulatory interpretations. To address these, this study proposes the first comprehensive compliance verification framework tailored to high-risk AI systems. Structured along two dimensions—“method type” (governance vs. testing) and “assessment object” (data, model, process, product)—the framework establishes a multi-layered, lifecycle-spanning verification paradigm. It introduces a novel mapping mechanism that systematically translates legal provisions into executable verification activities, integrating compliance engineering, law-technology alignment modeling, standards-mapping matrices, and risk-informed pathway design. The framework significantly reduces regulatory uncertainty, enhances cross-border assessment consistency, and enables coordinated governance among policymakers, auditors, and developers. (149 words)

This study addresses a critical gap in current AI governance, which predominantly emphasizes substantive rules while neglecting the legal and regulatory infrastructure necessary for their generation and implementation. For the first time, this work systematically positions legal infrastructure as the cornerstone of effective AI governance and proposes an institutional framework comprising a frontier model registration system, an autonomous agent identification mechanism, and a market-oriented regulatory service model. Through rigorous legal design, regulatory modeling, and policy mechanism analysis, the research delivers an actionable institutional pathway that significantly enhances the flexibility, scalability, and enforcement efficacy of AI governance rules.