ethical review

This paper identifies a core dilemma in organizational responsible AI governance: ambiguous responsibility boundaries across AI lifecycle stages and a lack of role- and stage-appropriate operational tools. Methodologically, the study systematically reviews over 220 responsible AI tools and proposes a novel two-dimensional (Actor, Stage) classification framework, integrating systematic review, meta-analysis, and qualitative coding. It identifies three critical governance gaps: (1) unclear accountability attribution, (2) absence of empirical validation for most tools, and (3) severe coverage imbalance across actors and stages. Results show that >80% of tools target developers during data and modeling phases; tools for leadership, deployers, end users, and stages such as value proposition definition and deployment are virtually absent. Moreover, >90% of tools lack empirical evidence. The study establishes a theoretically grounded, empirically benchmarked framework to advance actor–stage–aligned AI governance tool ecosystems.

As AI becomes increasingly embedded in safety-critical domains, its ethical risks have grown markedly. This paper systematically reviews six core ethical principles—fairness, privacy preservation, accountability, security and robustness, transparency and explainability, and environmental impact—and proposes the first integrative AI ethics review framework that bridges technical governance and socio-technical systems analysis. Through comprehensive literature synthesis, interdisciplinary theoretical integration, and comparative analysis of national AI policy documents, the study transcends a purely technical lens to develop a socio-technical analytical matrix spanning the AI lifecycle and addressing diverse stakeholders. The work unifies prevailing ethical concerns in AI societal deployment and delivers a theoretically grounded, value-sensitive, and practically actionable foundation for governance. It offers an original paradigm for global AI ethics governance, advancing both conceptual coherence and implementation readiness. (149 words)

This study investigates cross-role (e.g., engineers, product managers, ethics specialists) and cross-national (43 countries, N=414) variations in AI ethics awareness, policy comprehension, and risk mitigation practices within AI development teams. Employing a mixed-methods design, it integrates large-scale surveys with in-depth interviews, combining quantitative statistical analysis and qualitative thematic coding. Results reveal a pronounced role-based ethical responsibility gap and a non-uniform global distribution of regulatory sensitivity and implementation capacity. Building on these findings, the study proposes a “collaborative, role-sensitive ethics governance framework” that mandates multi-stakeholder engagement across the AI lifecycle and incorporates localization mechanisms for contextual adaptation. This framework advances AI ethics practice from prescriptive, one-size-fits-all guidelines toward inclusive, situated governance—offering an actionable, differentiated pathway for global AI policy implementation and responsible innovation.

Current AI ethics assessments are fragmented, focusing predominantly on fairness, transparency, privacy, and trust at the model or output level while neglecting inter-component system interactions, real-world harm contexts, and causal harm propagation pathways—resulting in evaluations disconnected from actual risk scenarios and lacking actionable thresholds. Method: Through a scoping review synthesizing nearly 800 ethics metrics, this study constructs the first four-dimensional relational framework—“System Components–Attributes–Risks–Harms”—to systematically map ethical assessment dimensions. Contribution/Results: The framework uncovers three critical gaps: insufficient system integration, weak contextual embedding, and poor actionability. It advances AI ethics evaluation from isolated metric measurement toward a systemic, traceable, and intervention-oriented paradigm—enhancing regulatory alignment and practical deployment efficacy in industry settings.

This study responds to the UN’s interim report on AI governance, addressing the dual challenge of harnessing AI to advance the Sustainable Development Goals (SDGs) while mitigating associated risks—including exacerbation of social inequality, ethical and environmental harms, and misalignment with international law and human rights standards. Method: It pioneers an integrative governance pathway embedding AI regulation within international legal frameworks, human rights norms, and the SDGs, employing interdisciplinary policy analysis, multilevel governance modeling, consistency assessment against international law, and socio-technical impact diagnostics. Contribution/Results: The project proposes a dual-track governance paradigm emphasizing legally binding instruments alongside AI literacy capacity-building. It formulates an actionable global AI governance principles framework and delivers 12 cross-border collaborative recommendations—subsequently adopted by multiple UN entities as policy reference benchmarks.

This study addresses the frequent neglect of environmental impacts in computationally intensive research—such as artificial intelligence—due to ambiguous ethical review policies. It presents the first systematic framework integrating environmental sustainability into the ethical oversight of computational research. By delineating clear review boundaries, establishing evidentiary standards, and developing researcher self-assessment tools, the framework enables institutional ethics committees to effectively evaluate the environmental costs of proposed projects. This approach provides actionable guidance for ethical review processes and encourages researchers to proactively consider the ecological footprint of their work during early design stages, thereby addressing a critical gap in current research ethics frameworks concerning sustainability.

Rapid AI advancement poses novel governance challenges, necessitating a rigorous, technically grounded approach to AI governance. Method: This work introduces “technical AI governance” as a distinct paradigm and establishes the first interdisciplinary analytical framework—integrating AI safety, mechanism design, policy modeling, and governance theory—to systematically address three core problem domains: risk identification, evaluation of intervention effectiveness, and compliance mechanism design. Adopting a problem-driven methodology, it clarifies how technical tools can concretely support governance practice. Contributions/Results: (1) A formal, structured definition of technical AI governance and a taxonomy of its core problems; (2) The first publicly available, extensible open-problems catalog for technical AI governance, bridging methodological gaps between technical and policy communities; and (3) An actionable, problem-oriented investment guide for researchers and funding agencies to prioritize high-impact technical governance research.

Responsible Artificial Intelligence (RAI) governance faces significant implementation challenges in globally distributed organizations, primarily due to the limited applicability of existing frameworks under complex organizational structures and decentralized decision-making authority. Method: This study proposes ARGO—a novel, adaptive, three-tier RAI governance framework that integrates centralized coordination with local autonomy and supports modular, context-sensitive deployment. Its design and efficacy were rigorously evaluated through a multi-case, cross-departmental assessment spanning diverse business units and AI application domains. Contribution/Results: The evaluation identified four recurrent barrier patterns impeding RAI implementation. Empirical findings demonstrate that ARGO significantly enhances accountability mechanisms, standardization consistency, and local adaptability. By reconciling global coherence with contextual flexibility, ARGO establishes a new governance paradigm for decentralized organizations seeking scalable, operationally viable RAI oversight.

Current ethical review systems struggle to address structural ethical risks in large-scale, interdisciplinary research due to insufficient capacity, inconsistent standards, and privacy constraints. This work proposes Mirror, a multi-agent framework that integrates normative understanding, an executable rule repository, and a multi-role collaborative deliberation mechanism to support both rapid compliance checks and in-depth committee-like evaluations. Leveraging a newly constructed domain-specific ethical QA dataset, EthicsQA, the authors fine-tune a specialized language model, EthicsLLM, and integrate it with a rule engine and a structured ethical dimension assessment framework. Experimental results demonstrate that the proposed approach significantly outperforms general-purpose large language models in evaluation quality, consistency, and domain expertise, making it suitable for projects ranging from minimal-risk studies to complex scientific endeavors.

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.

Rapid AI industrial deployment has outpaced ethical assessment, exposing systemic risks including accountability gaps, governance failures, poor data quality, weakened human oversight, insufficient technical robustness, and negative environmental and societal externalities (e.g., high energy consumption, exacerbation of inequality); regulatory ambiguity, low transparency, and excessive technical dependence further compound compliance and safety challenges. This study employs semi-structured interviews with 15 domain experts and a systematic literature review to identify critical tensions in data ethics, regulatory implementation, and governance practice. Its primary contribution is the novel “Dual-Dimensional Coordination” framework for trustworthy AI: Dimension One embeds regulatory compliance requirements, while Dimension Two aligns with local sociocultural values. The framework institutionalizes the coupling of transparency mechanisms and responsibility allocation, offering an actionable pathway toward a human-centered, robust, and sustainable AI ecosystem.

Current AI governance suffers from a structural gap between high-level regulatory principles and operational implementation, resulting in weak compliance and ineffective risk management. This paper proposes a five-layer, progressive AI governance framework that systematically bridges macro-level regulatory requirements, meso-level standardization, and micro-level certification—thereby closing the chasm between legislation, standards, and practice. The framework integrates compliance mapping, standardized assessment methodologies, and actionable certification mechanisms. Its efficacy is empirically validated through two representative use cases: AI fairness assurance and incident reporting. As the first model to enable end-to-end, structured alignment from principle to practice, it offers policymakers and industry stakeholders a governance pathway that balances global interoperability with regional adaptability. The framework significantly enhances AI system explainability, controllability, and societal trust. (149 words)