别再笼统说'AI',具体问题具体分析才有效
Stop Saying "AI"
- 用军事领域举例,拆解'军事AI'的多种系统类型
- 不同系统风险与收益各异,泛化批判无效
- 适合政策制定者和研究者精准讨论技术细节
在学术界、产业界和政府中,'AI'已成为研发、监管辩论和快速决策承诺的核心。然而,在安全关键领域,对'AI'可能影响决策、责任归属或出错概率的担忧日益增加。但多数批评针对的是一个宽泛术语'AI',其涵盖众多用于不同任务的系统,各自具有独特局限性、挑战和应用场景。本文以军事领域为例,提出一个松散的系统分类法,并讨论各类系统的挑战。强调:对某一类系统的批评不适用于其他系统。为使讨论有效推进,必须更精确,尽可能摒弃'AI'这一模糊表述。研究人员、开发者和政策制定者应明确所指系统及其潜在收益与风险。尽管以军事AI为例,但结论适用于所有'AI'相关讨论。
原文摘要 · Abstract (English)
Across academia, industry, and government, ``AI'' has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how ``AI'' may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally ``AI'', a broad term admitting many (types of) systems used for a variety of tasks and each coming with its own set of limitations, challenges, and potential use cases. In this article, we focus on the military domain as a case study and present both a loose enumerative taxonomy of systems captured under the umbrella term ``military AI'', as well as discussion of the challenges of each. In doing so, we highlight that critiques of one (type of) system will not always transfer to other (types of) systems. Building on this, we argue that in order for debates to move forward fruitfully, it is imperative that the discussions be made more precise and that ``AI'' be excised from debates to the extent possible. Researchers, developers, and policy-makers should make clear exactly what systems they have in mind and what possible benefits and risks attend the deployment of those particular systems. While we focus on AI in the military as an exemplar for the overall trends in discussions of ``AI'', the argument's conclusions are broad and have import for discussions of AI across a host of domains.
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