为计算机课程设计AI政策讨论框架,帮助学生应对复杂监管环境。
Economic Competition, EU Regulation, and Executive Orders: A Framework for Discussing AI Policy Implications in CS Courses
- 提出将AI政策讨论融入计算机课程的教学框架
- 对比美欧近期AI政策,揭示监管差异与冲突
- 强调技术实现与政策执行的衔接,适合教育者和政策研究者
人工智能技术的快速发展促使社会关注其负责任使用的治理路径。当前,大型企业和政府正通过政策表达并实施治理偏好,但现有文献显示AI伦理原则存在显著异质性。我们前期研究发现,当前计算机科学(CS)课程尚未纳入对AI政策影响的讨论。在此背景下,私营企业、地方、国家及跨国政府间的管辖重叠与政策矛盾,使AI治理环境日益复杂。因此,培养能够适应不断演变监管环境的AI开发者,已成为计算机教育的关键任务。本文旨在提出一个将新兴AI政策格局融入计算机课程的讨论框架:首先概述美国与欧盟近期的AI政策举措;随后提出适用于技术和非技术类课程的引导性问题。全文强调规范性政策要求与技术实现之间仍存挑战的关联,尤其是在代码与治理结构层面的执行难题。本研究为弥合AI政策与计算机教育之间的鸿沟提供了重要参考,凸显了培养具备社会政策响应能力的AI工程师的必要性。
原文摘要 · Abstract (English)
The growth and permeation of artificial intelligence (AI) technologies across society has drawn focus to the ways in which the responsible use of these technologies can be facilitated through AI governance. Increasingly, large companies and governments alike have begun to articulate and, in some cases, enforce governance preferences through AI policy. Yet existing literature documents an unwieldy heterogeneity in ethical principles for AI governance, while our own prior research finds that discussions of the implications of AI policy are not yet present in the computer science (CS) curriculum. In this context, overlapping jurisdictions and even contradictory policy preferences across private companies, local, national, and multinational governments create a complex landscape for AI policy which, we argue, will require AI developers able adapt to an evolving regulatory environment. Preparing computing students for the new challenges of an AI-dominated technology industry is therefore a key priority for the CS curriculum. In this discussion paper, we seek to articulate a framework for integrating discussions on the nascent AI policy landscape into computer science courses. We begin by summarizing recent AI policy efforts in the United States and European Union. Subsequently, we propose guiding questions to frame class discussions around AI policy in technical and non-technical (e.g., ethics) CS courses. Throughout, we emphasize the connection between normative policy demands and still-open technical challenges relating to their implementation and enforcement through code and governance structures. This paper therefore represents a valuable contribution towards bridging research and discussions across the areas of AI policy and CS education, underlining the need to prepare AI engineers to interact with and adapt to societal policy preferences.
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