The Ethical Need for Watermarks in Machine-Generated Language

Watermarks should be introduced in the natural language outputs of AI systems in order to maintain the distinction between human and machine-generated text. The ethical imperative to not blur this distinction arises from the asemantic nature of large language models and from human projections of emotional and cognitive states on machines, possibly leading to manipulation, spreading falsehoods or emotional distress. Enforcing this distinction requires unintrusive, yet easily accessible marks of the machine origin. We propose to implement a code based on equidistant letter sequences. While no such code exists in human-written texts, its appearance in machine-generated ones would prove helpful for ethical reasons

Lost in just the translation

This paper describes the design and implementation of a scheme for hiding information in translated natural language text, and presents experimental results using the implemented system. Unlike the previous work, which required the presence of both the source and the translation, the protocol presented in this paper requires only the translated text for recovering the hidden message. This is a significant improvement, as transmitting the source text was both wasteful of resources and less secure. The security of the system is now improved not only because the source text is no longer available to the adversary, but also because a broader repertoire of defenses (such as mixing human and machine translation) can now be used

ABSTRACT Lost in Just the Translation

This paper describes the design and implementation of a scheme for hiding information in translated natural language text, and presents experimental results using the implemented system. Unlike the previous work, which required the presence of both the source and the translation, the protocol presented in this paper requires only the translated text for recovering the hidden message. This is a significant improvement, as transmitting the source text was both wasteful of resources and less secure. The security of the system is now improved not only because the source text is no longer available to the adversary, but also because a broader repertoire of defenses (such as mixing human and machine translation) can now be used. 1