Three Inverse Laws of AI

人工智能的三条逆向法则

Three Inverse Laws of AI By Susam Pal on 12 Jan 2026 人工智能的三条逆向法则，作者：Susam Pal，2026年1月12日

Introduction 引言

Since the launch of ChatGPT in November 2022, generative artificial intelligence (AI) chatbot services have become increasingly sophisticated and popular. These systems are now embedded in search engines, software development tools as well as office software. For many people, they have quickly become part of everyday computing. These services have turned out to be quite useful, especially for exploring unfamiliar topics and as a general productivity aid. However, I also think that the way these services are advertised and consumed can pose a danger to society, especially if we get into the habit of trusting their output without further scrutiny. 自2022年11月ChatGPT发布以来，生成式人工智能（AI）聊天机器人服务变得日益复杂且广受欢迎。这些系统现已嵌入搜索引擎、软件开发工具以及办公软件中。对许多人而言，它们已迅速成为日常计算的一部分。事实证明，这些服务非常有用，特别是在探索陌生领域和作为通用生产力辅助工具方面。然而，我认为这些服务的广告宣传和使用方式可能会对社会构成威胁，尤其是当我们养成不加审视就信任其输出结果的习惯时。

Pitfalls 陷阱

Certain design choices in modern AI systems can encourage uncritical acceptance of their output. For example, many popular search engines are already highlighting answers generated by AI at the very top of the page. When this happens, it is easy to stop scrolling, accept the generated answer and move on. Over time, this could inadvertently train users to treat AI as the default authority rather than as a starting point for further investigation. I wish that each such generative AI service came with a brief but conspicuous warning explaining that these systems can sometimes produce output that is factually incorrect, misleading or incomplete. Such warnings should highlight that habitually trusting AI output can be dangerous. In my experience, even when such warnings exist, they tend to be minimal and visually deemphasised. 现代人工智能系统中的某些设计选择可能会助长人们对其输出结果的盲目接受。例如，许多主流搜索引擎已经在页面最上方突出显示由人工智能生成的答案。当这种情况发生时，用户很容易停止滚动页面，直接接受生成的答案并继续操作。久而久之，这可能会在无意中训练用户将人工智能视为默认的权威，而非进一步调查的起点。我希望每一项此类生成式人工智能服务都能附带简短但醒目的警告，说明这些系统有时会产生事实错误、误导性或不完整的内容。此类警告应强调，习惯性地信任人工智能的输出可能是危险的。根据我的经验，即使存在此类警告，它们往往也微不足道且在视觉上被弱化了。

Inverse Laws of Robotics 机器人逆向法则

In the world of science fiction, there are the Three Laws of Robotics devised by Isaac Asimov, which recur throughout his work. These laws were designed to constrain the behaviour of robots in order to keep humans safe. As far as I know, Asimov never formulated any equivalent laws governing how humans should interact with robots. I think we now need something to that effect to keep ourselves safe. I will call them the Inverse Laws of Robotics. These apply to any situation that requires us humans to interact with a robot, where the term ‘robot’ refers to any machine, computer program, software service or AI system that is capable of performing complex tasks automatically. I use the term ‘inverse’ here not in the sense of logical negation but to indicate that these laws apply to humans rather than to robots. 在科幻小说世界中，艾萨克·阿西莫夫（Isaac Asimov）提出了“机器人三定律”，这些定律在他的作品中反复出现。这些定律旨在约束机器人的行为，以保障人类的安全。据我所知，阿西莫夫从未制定过任何关于人类应如何与机器人互动的等效定律。我认为我们现在需要类似的准则来保护自身安全。我将其称为“机器人逆向法则”。这些法则适用于任何需要人类与机器人互动的场景，其中“机器人”一词指代任何能够自动执行复杂任务的机器、计算机程序、软件服务或人工智能系统。我在此使用“逆向”一词并非指逻辑上的否定，而是为了表明这些法则适用于人类而非机器人。

It is well known that Asimov’s laws were flawed. Indeed, Asimov used those flaws to great effect as a source of tension. But the particular ways in which they fail for fictional robots do not necessarily carry over to these inverse laws for humans. Asimov’s laws try to constrain the behaviour of autonomous robots. However, these inverse laws are meant to guide the judgement and conduct of humans. Still, one thing we can learn from Asimov’s stories is that no finite set of laws can ever be foolproof for the complex issues we face with AI and robotics. But that does not mean we should not even try. There will always be edge cases where judgement is required. A non-exhaustive set of principles can still be useful if it helps us think more clearly about the risks involved. 众所周知，阿西莫夫的定律存在缺陷。事实上，阿西莫夫巧妙地利用这些缺陷作为故事冲突的来源。但这些定律在虚构机器人身上失效的具体方式，并不一定适用于人类的这些逆向法则。阿西莫夫的定律试图约束自主机器人的行为，而这些逆向法则旨在指导人类的判断和行为。尽管如此，我们可以从阿西莫夫的故事中学到一点：面对人工智能和机器人技术带来的复杂问题，没有任何一套有限的定律是万无一失的。但这并不意味着我们不应尝试。总会有需要人类判断的极端情况出现。如果一套非详尽的原则能帮助我们更清晰地思考相关风险，那么它依然是有用的。

Inverse Laws of Robotics 机器人逆向法则

Here are the three inverse laws of robotics: 以下是机器人逆向法则的三条内容：

1. Humans must not anthropomorphise AI systems.
2. 人类不得将人工智能系统拟人化。
3. Humans must not blindly trust the output of AI systems.
4. 人类不得盲目信任人工智能系统的输出。
5. Humans must remain fully responsible and accountable for consequences arising from the use of AI systems.
6. 人类必须对使用人工智能系统所产生的后果承担全部责任。

Non-Anthropomorphism 非拟人化

Humans must not anthropomorphise AI systems. That is, humans must not attribute emotions, intentions or moral agency to them. Anthropomorphism distorts judgement. In extreme cases, anthropomorphising can lead to emotional dependence. Modern chatbot systems often sound conversational and empathetic. They use polite phrasing and conversational patterns that closely resemble human interaction. While this makes them easier and more pleasant to use, it also makes it easier to forget what they actually are: large statistical models producing plausible text based on patterns in data. I think vendors of AI based chatbot services could do a better job here. In many cases, the systems are deliberately tuned to feel more human rather than more mechanical. I would argue that the opposite approach would be healthier in the long term. A slightly more robotic tone would reduce the likelihood that users mistake fluent language for understanding, judgement or intent. Whether or not vendors make such changes, it still serves us well, I think, to avoid this pitfall ourselves. We should actively resist the habit of treating AI systems as social actors or moral agents. Doing so preserves clear thinking about their capabilities and limitations. 人类不得将人工智能系统拟人化。也就是说，人类不得赋予它们情感、意图或道德主体性。拟人化会扭曲判断。在极端情况下，拟人化可能导致情感依赖。现代聊天机器人系统听起来往往具有对话感和同理心。它们使用礼貌的措辞和与人类互动极为相似的对话模式。虽然这使得它们更易于使用且体验更佳，但也更容易让人忘记它们的本质：即基于数据模式生成合理文本的大型统计模型。我认为人工智能聊天机器人服务提供商在这方面可以做得更好。在许多情况下，这些系统被刻意调整得更像人类而非机器。我认为，从长远来看，采取相反的方法会更健康。稍微多一点机械感可以降低用户将流畅的语言误认为理解、判断或意图的可能性。无论供应商是否做出此类改变，我认为我们自己避免陷入这个陷阱依然是有益的。我们应积极抵制将人工智能系统视为社会参与者或道德主体的习惯。这样做有助于保持对它们能力和局限性的清醒认识。

Non-Deference 非盲从

Humans must not blindly trust the output of AI systems. AI-generated content must not be treated as authoritative without independent verification appropriate to its context. This principle is not unique to AI. In most areas of life, we should not accept information uncritically. In practice, of course, this is not always feasible. Not everyone is an expert in medicine or law, so we often rely on trusted institutions and public health authorities for guidance. However, the guidance published by such institutions is in most cases peer reviewed by experts in their fields. On the other hand, when we receive an answer to a question from an AI chatbot in a private chat session, there has been no peer review of the particular stochastically generated response presented to us. Therefore, the onus of critically examining the response falls on us. Although AI systems today have become quite impressive at certain tasks, they are still known to produce output that would be a mistake to rely on. Even if AI systems improve to the point of producing reliable output with a high degree of likelihood, due to their inherent stochastic nature, there… 人类不得盲目信任人工智能系统的输出。在没有进行适当的独立验证之前，人工智能生成的内容不得被视为权威。这一原则并非人工智能所独有。在生活的大多数领域，我们都不应不加批判地接受信息。当然，在实践中，这并不总是可行的。并非每个人都是医学或法律专家，因此我们经常依赖受信任的机构和公共卫生部门来获取指导。然而，这些机构发布的指导意见在大多数情况下都经过了领域专家的同行评审。另一方面，当我们通过私人聊天会话从人工智能聊天机器人那里获得问题答案时，呈现给我们的特定随机生成响应并未经过任何同行评审。因此，批判性审查该响应的责任落在了我们自己身上。尽管当今的人工智能系统在某些任务上表现得相当出色，但众所周知，它们仍会产生不应被依赖的输出结果。即使人工智能系统改进到能够以极高概率产生可靠输出的程度，由于其固有的随机性，……