[论文解读] Computing Power and the Governance of Artificial Intelligence

Girish Sastry, Lennart Heim+17 more|arXiv (Cornell University)|Feb 13, 2024

Ethics and Social Impacts of AI被引用 20

一句话总结

本文主张对 AI 计算资源开展治理可以通过提升监管可见性、将资源分配用于安全用途、以及对滥用的执法等方面，切实增强 AI 治理，同时勾勒出防护措施和风险。

ABSTRACT

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.

研究动机与目标

• 激励研究将计算视为 AI 治理的杠杆，并阐明为何计算在干预方面尤为有效。
• 界定 AI 三元组（数据、算法、计算）并解释计算在前沿模型与部署中的作用。
• 概述计算治理如何提升可见性、配置与执法这三种治理能力。
• 识别计算治理的潜在风险及防护措施，并讨论当前与未来的政策选项。

提出的方法

• 建立一个概念框架，将计算视为治理杠杆，基于其可检测性、可排他性、可量化性以及供应链集中度。
• 描述 AI 生命周期（设计、训练、增强、部署）并将计算足迹映射到各阶段。
• 回顾现有的计算治理努力（国内算力、出口管制、报告阈值）以及国内外政策工具。
• 提出跨越可见性、配置和执法类别的示例治理机制，并附设计考量。
• 讨论计算治理的风险（隐私、集权、可行性），并提出防护措施以降低这些风险。

Figure 1 : Summary of the core concepts in the report. Compute is attractive for policymaking because of four properties. These properties can be leveraged to design and implement policies that enable three critical capacities for the governance of AI.

实验结果

研究问题

• RQ1 如何通过计算治理提高对 AI 能力与用途的监管可见性？
• RQ2 哪些政策机制可以重新配置或管理算力，以促进安全、有益的 AI 发展？
• RQ3 如何通过计算来强制执行 AI 发展与部署的规范和法规？
• RQ4 在计算治理中需要哪些防护措施以降低隐私与集权风险？
• RQ5 基于计算治理方法的实际挑战和就绪程度有哪些？

主要发现

• 由于其集中、具象的特性及可量化的足迹，计算对前沿 AI 模型具有高度影响。
• 计算治理可以增强 AI 治理的三种能力：可见性、配置与执法。
• 存在多种用于可见性的示例机制（如报告要求、 AI 芯片注册等）、用于配置的机制（如对有益 AI 的定向支持、节奏调整），以及用于执法的机制（如基于硬件的控制、规范执行）。
• 存在显著的风险和可行性担忧，包括隐私、数据泄露以及权力可能的集中，需要防护措施，如排除小规模计算并强调隐私保护实践。
• 当前的治理努力（国内能力、出口管制、报告阈值）既显示出动力，也存在紧张局势，凸显需要一个整体性的理论框架和对基于计算的政策进行周密设计。

Figure 2 : The AI Triad. The three key technical inputs to AI are data, algorithms, and compute. Human capital is required for all inputs.