让生成式AI符合行业标准，提升监管与运营合规性。
Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance
- 构建评估框架，量化大模型对各类标准的符合度。
- 发现当前主流GenAI在法律、医疗等领域的合规能力不足。
- 为标准制定者与AI使用者提供可落地的整合建议。
技术标准是确保系统与流程互操作性、质量与准确性的已文档化规范。近年来，生成式AI（GenAI）应用迅速普及，广泛渗透至工程、法律、医疗、教育等以标准为导向的行业。本文评估了不同领域标准的重要性等级，并对当前先进GenAI模型的合规能力进行评级。通过分析集成GenAI实现标准合规所面临的挑战与机遇，提出具体可行的改进建议。研究认为，通过计算方法将GenAI与标准对齐，有助于强化监管与运营合规性。我们预期该方向将在未来数年内成为大型、强能力GenAI系统管理、监督与可信度的核心议题。
原文摘要 · Abstract (English)
Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.
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