通用人工智能在欧盟人工智能法案中的不确定性估计困境
The Dilemma of Uncertainty Estimation for General Purpose AI in the EU AI Act
AI总结：
本文探讨欧盟《人工智能法案》下通用人工智能的不确定性估计困境：该方法有助于合规，但可能因计算量超阈值而将模型归类为系统性风险系统。
摘要：
《人工智能法案》是欧盟范围内对人工智能系统的监管法规。该法案包含针对通用人工智能模型的特定条款，但这些条款仍需根据技术标准和最新研究进行进一步解释，以确保切实可行的合规解决方案。本文审查了《人工智能法案》对通用人工智能提供者和部署者的要求，并进一步提出将不确定性估计作为此类模型训练中法律合规和质量保证的适当措施。我们认为，不确定性估计应成为在现实世界中部署模型的必要组成部分，并且在欧盟《人工智能法案》下，它可以满足透明度、准确性和可信赖性方面的若干要求。然而，通常使用不确定性估计方法会增加计算量，从而产生一个困境，因为计算量可能超过阈值（$10^{25}$ FLOPS），从而将模型归类为承担更多监管负担的系统性风险系统。
英文摘要：
The AI act is the European Union-wide regulation of AI systems. It includes specific provisions for general-purpose AI models which however need to be further interpreted in terms of technical standards and state-of-art studies to ensure practical compliance solutions. This paper examines the AI act requirements for providers and deployers of general-purpose AI and further proposes uncertainty estimation as a suitable measure for legal compliance and quality assurance in training of such models. We argue that uncertainty estimation should be a required component for deploying models in the real world, and under the EU AI Act, it could fulfill several requirements for transparency, accuracy, and trustworthiness. However, generally using uncertainty estimation methods increases the amount of computation, producing a dilemma, as computation might go over the threshold ($10^{25}$ FLOPS) to classify the model as a systemic risk system which bears more regulatory burden.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
