发表机构
Panteion University of Social and Political Sciences（潘泰翁社会与政治科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究对可信人工智能工具和标记框架进行批判性分析，利用经合组织数据集，通过实证映射等方法找出伦理重点等方面的不对称，发现当前存在的问题，建议扩大伦理目标、贯穿伦理于生命周期并促进多利益相关者参与，以推动人工智能治理。
AI 中文摘要
随着人工智能系统对社会影响日增，确保其符合伦理及可信部署成全球要务。虽已有众多高层伦理准则，但仍有人批评其抽象且缺乏具体实施机制。本文利用经合组织的综合数据集，对旨在实现可信人工智能的工具和信任标记框架进行批判性分析。通过实证映射和描述性比较分析，我们发现了在伦理重点、生命周期覆盖范围、利益相关者定位和工具类型方面的显著不对称。我们的研究结果表明，目前对公平、透明度和鲁棒性的强调较多，而对可解释性、数字安全和环境可持续性的关注较少。此外，大多数工具和认证集中在开发后阶段，对早期设计或数据收集阶段的指导有限。教育倡议和政策参与明显不足，这表明当前可信人工智能的努力主要由行业背景下的技术和程序措施主导。我们认为，弥合人工智能原则与实践之间持续存在的差距需要扩大伦理目标，将伦理贯穿人工智能生命周期，并促进更广泛的多利益相关者参与。本研究既诊断了现有的实施差距，又为推进更全面、包容和可执行的人工智能治理提供了可操作的建议。
英文摘要
As artificial intelligence (AI) systems increasingly impact society, ensuring their ethical and trustworthy deployment has become a global priority. While a myriad of high-level ethical guidelines have emerged, criticism persists that these frameworks remain abstract and lack concrete mechanisms for implementation. This paper conducts a critical analysis of tools and trust mark frameworks intended to operationalize trustworthy AI (TAI), drawing on a comprehensive dataset from the OECD. Through empirical mapping and descriptive comparative analysis, we identify significant asymmetries in ethical focus, lifecycle coverage, stakeholder targeting, and tool typology. Our findings show a strong emphasis on fairness, transparency, and robustness, with comparatively little attention paid to explainability, digital security, and environmental sustainability. Moreover, most tools and certifications concentrate on post-development stages, with limited guidance for early design or data collection phases. Educational initiatives and policy engagement are notably underdeveloped, suggesting that current TAI efforts are dominated by technical and procedural measures within industry contexts. We argue that bridging the persistent chasm between AI principles and practice requires expanding ethical objectives, embedding ethics across the AI lifecycle, and fostering broader multi-stakeholder participation. This study provides both a diagnosis of existing implementation gaps and actionable recommendations for advancing more holistic, inclusive, and enforceable AI governance
