企业为何难对LLM输出错误免责？2024责任分析指南

Introduction

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) are being integrated into a wide array of enterprise applications, from customer service chatbots to content generation and data analysis tools. While these models offer unprecedented efficiency and scalability, they also introduce significant legal and ethical challenges. A critical question emerges: can an enterprise that utilizes an LLM to generate outputs for consumers effectively disclaim liability when those outputs cause harm?

This article argues that enterprises will face substantial, if not insurmountable, hurdles in disclaiming responsibility for consumer harm resulting from LLM-generated misstatements. This holds true even in scenarios where the underlying model is developed and maintained by a third party and its outputs are inherently probabilistic. The core of the issue lies not in the technology's ownership but in the enterprise's role in selecting, deploying, and presenting the AI's outputs to end-users.

在人工智能快速发展的格局中，大型语言模型（LLMs）正被集成到从客户服务聊天机器人到内容生成和数据分析工具的各种企业应用中。虽然这些模型提供了前所未有的效率和可扩展性，但它们也带来了重大的法律和伦理挑战。一个关键问题随之出现：当利用LLM为消费者生成内容的企业，在这些内容造成损害时，能否有效地免除责任？

本文认为，对于由LLM生成的错误陈述导致的消费者损害因产品或服务缺陷导致的消费者人身伤害、财产损失或其他合法权益受损。，企业将在免除责任方面面临巨大、甚至难以逾越的障碍。即使基础模型由第三方开发维护且其输出本质上是概率性的，这一论点依然成立。问题的核心不在于技术的所有权，而在于企业在选择、部署和向最终用户呈现AI输出过程中所扮演的角色。

Key Legal and Conceptual Challenges

The inability to disclaim responsibility stems from several interconnected principles in law, consumer protection, and risk management.

无法免除责任源于法律、消费者保护和风险管理中几个相互关联的原则。

1. The Enterprise as the "Publisher" or "Speaker"

When an enterprise integrates an LLM's output into its customer-facing operations—be it a response on its website, advice in its app, or content in its marketing materials—it effectively adopts that output as its own communication. Legally, the enterprise becomes the publisher or speaker of that information. Courts and regulators are likely to view the enterprise, not the model's creator, as the entity with a direct relationship with the consumer and the one that presented the potentially harmful information. Disclaimers buried in terms of service that attempt to shift blame to an "unreliable AI" are unlikely to override this fundamental principle of accountability for one's own published statements.

当企业将LLM的输出集成到其面向客户的业务中时——无论是其网站上的回复、应用程序中的建议还是营销材料中的内容——它实际上将该输出采纳为自己的沟通信息。从法律上讲，企业成为该信息的发布者或陈述者。法院和监管机构很可能将企业（而非模型的创建者）视为与消费者有直接关系并呈现了潜在有害信息的实体。隐藏在服务条款中、试图将责任归咎于“不可靠的AI”的免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。，不太可能推翻这种对自身发布陈述负责的基本原则。

2. Probabilistic Nature Does Not Excuse Negligence

The argument that "the model is probabilistic and can make mistakes" is a description of the technology's limitation, not a legal defense. Enterprises have a duty of care in their operations. Choosing to deploy a system known to produce confident but occasionally incorrect outputs without adequate safeguards, human oversight, or clear warnings to users could be construed as negligence. The focus shifts to whether the enterprise took reasonable steps to mitigate foreseeable risks associated with the technology's use, not whether the technology itself is perfect.

“模型是概率性的，可能会犯错”这一论点是对技术局限性的描述，而非法律辩护理由。企业在运营中负有注意义务。选择部署一个已知会产生自信但偶尔不正确输出的系统，而没有充分的保障措施、人工监督或对用户的明确警告，可能被解释为疏忽。焦点将转向企业是否采取了合理措施来减轻与技术使用相关的可预见风险，而不是技术本身是否完美。

3. Consumer Protection and Reasonable Expectations

Consumer protection laws are designed around the reasonable expectations of a consumer. A user interacting with an enterprise's official chatbot or help system reasonably expects that the information provided is endorsed by, and reliable for, that enterprise. They are not expected to understand the intricacies of third-party APIs or statistical model hallucinations. If harmful advice (e.g., incorrect medical, financial, or legal information) leads to consumer detriment, regulators will assess the situation based on this reasonable expectation and the resulting harm, not the technical architecture behind the scene.

消费者保护法是围绕消费者的合理期望设计的。与企业官方聊天机器人或帮助系统交互的用户，合理地期望所提供的信息得到该企业的认可且是可靠的。他们不被要求理解第三方API或统计模型幻觉的复杂性。如果有害的建议（例如，不正确的医疗、金融或法律信息）导致消费者受损，监管机构将根据这种合理期望及所造成的损害来评估情况，而非幕后的技术架构。

4. Failure of "Intermediate Service Provider" Defenses

Some enterprises may hope to rely on legal safe harbors designed for passive intermediaries (like internet service providers or web hosts that merely transmit content). However, these defenses typically require the entity to have no editorial control or active role in shaping the content. An enterprise that prompts an LLM, fine-tunes it on its own data, curates its outputs, and decides where and how to deploy it is exercising significant editorial control. This active role in the content generation pipeline severely weakens any claim to being a mere passive intermediary.

一些企业可能希望依赖为被动中介（如仅传输内容的互联网服务提供商或网络主机）设计的法律安全港。然而，这些辩护通常要求实体对内容没有编辑控制或积极的塑造作用。一个对LLM进行提示、用自己的数据对其进行微调、筛选其输出并决定在何处及如何部署它的企业，正在行使重要的编辑控制权。这种在内容生成流程中的积极作用，严重削弱了其作为纯粹被动中介的任何主张。

Main Analysis: The Path to Liability

The convergence of these factors creates a clear path to enterprise liability.

这些因素的汇聚为企业责任铺就了一条清晰的道路。

Step 1: Deployment and Presentation. The enterprise makes a conscious business decision to use an LLM to generate outputs for consumer consumption. This act initiates a duty of care.

步骤一：部署与呈现。 企业做出有意识的商业决策，使用LLM为消费者生成输出。这一行为启动了注意义务。

Step 2: Foreseeable Risk. The probabilistic and occasionally erroneous nature of LLMs is a well-known, foreseeable risk within the industry. Enterprises cannot claim they were unaware of the potential for misstatements.

步骤二：可预见风险。 LLMs的概率性和偶尔出错的性质是该行业内众所周知、可预见的风险。企业不能声称他们不了解错误陈述的可能性。

Step 3: Harm Realization. A consumer relies on an LLM-generated misstatement (e.g., incorrect dosage advice, faulty financial guidance, defamatory content) and suffers tangible harm (physical, financial, reputational).

步骤三：损害实现。 消费者依赖LLM生成的错误陈述（例如，不正确的剂量建议、错误的财务指导、诽谤性内容）并遭受了实际损害（身体、财务、声誉）。

Step 4: Attribution and Failure to Mitigate. The harmed consumer sues the enterprise they interacted with. The court examines whether the enterprise, as the publisher, took reasonable steps to prevent such harm. Evidence considered includes:

The presence and clarity of disclaimers.
The level of human oversight and guardrails implemented.
The training and monitoring of the AI system.
Whether the enterprise knowingly deployed the system in a high-risk domain without sufficient safeguards.

Given the enterprise's active role, the known risks of the technology, and the likely insufficiency of boilerplate disclaimers, courts will be inclined to find that the enterprise failed in its duty of care and is therefore liable for the resulting damages.

步骤四：归责与风险缓解失败。 受损的消费者起诉与之交互的企业。法院审查作为发布者的企业是否采取了合理措施来防止此类损害。考量的证据包括：

免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。的存在及其清晰度。

实施的人工监督和防护栏级别。

对AI系统的培训和监控。

企业是否在明知的情况下，在没有足够保障措施的高风险领域部署了该系统。

鉴于企业的积极作用、该技术的已知风险以及格式免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。很可能不充分，法院将倾向于认定企业未能履行其注意义务，因此应对所造成的损害负责。

Conclusion and Recommendations

The notion that an enterprise can serve AI-generated content under a blanket disclaimer of accuracy is a legal fantasy. As LLMs become more deeply embedded in business processes, the associated liability risks crystallize. Enterprises must proactively manage this risk, not attempt to disclaim it away.

认为企业可以在笼统的准确性免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。下提供AI生成内容的观点是一种法律幻想。随着LLMs更深入地嵌入业务流程，相关的责任风险也日益明确。企业必须主动管理这种风险，而不是试图通过免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。来规避它。

Recommendations for Enterprises:

Implement Robust Guardrails: Develop and deploy technical and procedural safeguards to filter, fact-check, and constrain LLM outputs, especially in high-stakes domains (Legal, Medical, Financial).

实施强有力的防护栏： 制定并部署技术和程序性保障措施，以过滤、事实核查和约束LLM输出，特别是在高风险领域（法律、医疗、金融）。
Ensure Human-in-the-Loop: Maintain meaningful human oversight for critical outputs. Define clear escalation paths for uncertain or high-risk responses.

确保人在回路中： 对关键输出保持有效的人工监督。为不确定或高风险的回复定义清晰的升级路径。
Craft Transparent Communications: Use clear, context-appropriate warnings that inform users they are interacting with an AI that may make mistakes. Avoid legalese that seeks to absolve all responsibility.

制定透明的沟通方式： 使用清晰、符合语境的警告，告知用户他们正在与一个可能犯错的AI交互。避免试图免除所有责任的法律措辞。
Secure Appropriate Insurance: Review and update insurance policies (e.g., Errors & Omissions, Cyber Liability) to ensure coverage for novel AI-related risks.

获取适当的保险： 审查并更新保险单（例如，错误与遗漏险、网络责任险），以确保涵盖新型的AI相关风险。
Treat AI as a Product Component: Subject AI-driven features to the same rigorous risk assessment, quality assurance, and compliance reviews as any other product or service component.

将AI视为产品组件： 对AI驱动的功能进行与任何其他产品或服务组件同样严格的风险评估、质量保证和合规审查。

Ultimately, responsibility follows control and benefit. Since enterprises control the deployment and reap the benefits of LLM integration, they will be held accountable for the foreseeable harms that result. Prudent governance, not creative disclaimers, is the only sustainable path forward.

最终，责任伴随控制和利益而来。既然企业控制着部署并享有LLM集成的利益，它们就必须对由此产生的可预见损害负责。审慎的治理，而非创造性的免责声明企业试图免除或限制对LLM输出错误所造成损害的法律责任声明。，才是唯一可持续的前进道路。