evaluate regulatory impact

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 identifies systemic inconsistencies between Instagram’s official reporting under the EU’s Digital Services Act (DSA) and its actual content moderation practices and platform governance operations. Method: We propose a multi-layered consistency analysis framework that dynamically evaluates platforms within interconnected digital ecosystems, integrating data cross-verification with qualitative comparative analysis to overcome limitations of traditional isolated procedural audits. Contribution/Results: The study provides the first empirical evidence of Instagram’s compliance gaps across risk disclosure, algorithmic transparency, and moderation efficacy—demonstrating the framework’s effectiveness in early-stage compliance risk identification. The framework yields an actionable audit tool for DSA enforcement, advancing platform governance assessment from formal compliance toward substantive consistency.

This study addresses the limitation of existing regulatory comment analyses, which typically operate at a coarse granularity and thus fail to assess the actual impact of public input on specific regulatory obligations. To overcome this, the authors propose an Obligation-Level Responsiveness Auditing Framework—the first approach enabling auditable measurement of responsiveness at the level of individual obligations. Leveraging NLP techniques, the framework extracts and aligns specific obligations from proposed and final rules with corresponding public comments, while blinded human review distinguishes substantive from editorial modifications. Empirical analysis of 36 EPA rules and over 70,000 comments reveals that organizational commenters are more likely to elicit editorial adjustments than substantive changes, with commenter stance showing no significant effect. Crucially, disparities in commenters’ capacity to identify and challenge specific obligations emerge as a key determinant of equity in regulatory responsiveness.

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 study addresses the challenge in agent-based policy simulation of disentangling whether regulatory effects stem from agent adaptation, policy adaptation, or their interaction. To this end, the authors construct a controllable simulation benchmark using a single, configurable emission-regulation agent-based model (ABM) to systematically compare four combinations of static/adaptive agents and static/adaptive policies. They propose an evaluation paradigm centered on “institutional distinguishability,” integrating scalar metrics, symbolic diagnostics, trajectory patterns, and visual analytics to uncover hidden mechanistic differences despite similar average performance. Experiments replicate characteristic behaviors of various adaptive controllers—such as set-point, safety-margin, and one-sided control—and demonstrate that reliance solely on aggregate outcomes can obscure critical structural distinctions, thereby underscoring the necessity of comprehensive policy mechanism evaluation.

Current evaluations of AI governance proposals often fall into binary oppositions, overlooking implicit value trade-offs and lacking transparent analytical tools. This work proposes a multidimensional policy analysis framework that integrates expert interviews with computational text analysis to construct an interpretable scoring system across policy attributes, enabling cross-proposal comparison through visualization. Its novelty lies in three aspects: first, a multidimensional evaluation approach that avoids predetermined conclusions and explicitly reveals inherent trade-offs; second, a transparent hybrid methodology combining qualitative expert insights with quantitative computational validation; and third, the introduction of a domain-calibrated model as a benchmark against general-purpose large language models. The framework enables comparable, interpretable assessments of AI governance proposals across multiple attributes, allowing stakeholders to evaluate proposal relevance and coherence according to their own normative priorities.

This study addresses the sequential decision-making challenge firms face under stringent regulatory regimes when balancing compliance costs against data value in cross-border data flows. The authors propose a regime-anchored decision support system that translates regulatory requirements into computable minimal compliance mappings and models weekly corporate decisions via a finite-horizon Markov decision process, treating compliance as a hard constraint rather than a penalty term. Innovatively integrating masked deep reinforcement learning with counterfactual path advantage analysis, the framework enables efficient optimization and interpretable decision-making while supporting transferability across jurisdictions. Experimental results demonstrate that the learned policies outperform baseline approaches, exhibit high interpretability and auditability, and uncover key behavioral patterns such as an “absorb–adjust” effect and dynamic shifts in localization boundaries.