AI 中文总结
研究探讨水印能否应对人工智能生成内容相关认知和伦理挑战，认为扩展水印方法错误，提出以过程透明度和信息素养为核心的替代方法，能更有效应对人工智能生成虚假信息问题，重新定义作者身份。
AI 中文摘要
水印通常被视为区分人工智能生成内容和人类生成内容的直接解决方案，能让平台和监管机构追踪合成内容并大规模检测人工智能生成的输出。本文探讨这种机制是否能有效应对那些核心关注点并非内容生产自动化，而是信息准确性、意图和欺骗潜力的领域中出现的认知和伦理挑战。我们认为将基于水印的方法扩展到这些场景在概念和实践上都是错误的。隐形水印仅编码模型来源，转化为可见的人工智能生成标签时，会将复杂的创作过程简化为具有误导性的二元划分，且不提供真实性信息。此类标签可能会给生成工具的合法使用带来污名化，同时助长对无标记内容的不当信任。我们在此提出一种以过程透明度和信息素养为核心的替代方法。我们认为这些措施比可见水印标签更有效地应对了人工智能生成的虚假信息的认知和伦理层面问题，将作者身份重新定义为一种透明的人类实践，而非机器参与的二元指标。
英文摘要
Watermarking is often presented as a straightforward solution for distinguishing AI-generated from human-generated content, enabling platforms and regulators to trace synthetic content and detect AI-generated outputs at scale. This paper examines whether such mechanisms meaningfully address the epistemic and ethical challenges that arise in domains where the central concern is not the automation of content production, but the accuracy, intent, and deceptive potential of messages. We argue that extending watermark-based approaches to these settings is conceptually and practically misguided. Invisible watermarking encodes only model origin; when operationalized into visible AI-generated labels, it reduces complex creative processes to a misleading binary and provides no information about truthfulness. Such labels may stigmatize legitimate uses of generative tools while encouraging misplaced trust in unmarked content. Here we propose an alternative approach centered on process transparency and information literacy. We argue that these measures address the epistemic and ethical dimensions of AI-generated disinformation more effectively than visible watermark labels, reframing authorship as a transparent human practice rather than a binary indicator of machine involvement.
