用算法自动实现AI监管合规，提升速度与规模。
Computational Compliance for AI Regulation: Blueprint for a New Research Domain
- 提出全生命周期的计算式合规算法框架。
- 设计可量化评估算法性能的基准数据集。
- 为AI监管研究提供可落地的新方向，适合政策与技术交叉研究者。
AI监管时代已然到来，但若继续依赖传统人工方式，难以在必要速度与规模下实现合规。我们主张，唯有通过贯穿AI系统全生命周期的计算型合规算法，才能在动态环境下自动引导系统满足监管要求。然而，当前研究尚未明确此类算法应如何设计或如何评估。为此，本文提出一套算法设计目标，并构建了一个可用于定量衡量算法是否满足这些目标的基准数据集。本工作旨在为这一尚未成形的重要研究领域提供蓝图，推动该方向的学术投入与实践发展。
原文摘要 · Abstract (English)
The era of AI regulation (AIR) is upon us. But AI systems, we argue, will not be able to comply with these regulations at the necessary speed and scale by continuing to rely on traditional, analogue methods of compliance. Instead, we posit that compliance with these regulations will only realistically be achieved computationally: that is, with algorithms that run across the life cycle of an AI system, automatically steering it toward AIR compliance in the face of dynamic conditions. Yet despite their (we would argue) inevitability, the research community has yet to specify exactly how these algorithms for computational AIR compliance should behave - or how we should benchmark their performance. To fill these gaps, we specify a set of design goals for such algorithms. In addition, we specify a benchmark dataset that can be used to quantitatively measure whether individual algorithms satisfy these design goals. By delivering this blueprint, we hope to give shape to an important but uncrystallized new domain of research - and, in doing so, incite necessary investment in it.
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