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The Ethical Need for Watermarks in Machine-Generated Language

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arxiv 2209.03118 v1 pith:7NWNKCIY submitted 2022-09-07 cs.CL cs.CY

Alexei Grinbaum , Laurynas Adomaitis This is my paper · ORCID
classification cs.CLcs.CY
keywords distinctionethicallanguagemachine-generatedcodeemotionalhumanwatermarks
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Watermarking Quality for LLMs via Contextual Generation States Awareness

    cs.CR 2025-06 conditional novelty 6.0 of 10

    A context-aware plug-in for LLM watermarking that skips or weakens watermarks on semantically critical tokens, improving task accuracy at similar detection rates.

  2. Temperature Matters: Enhancing Watermark Robustness Against Paraphrasing Attacks

    cs.CL 2025-06 reject novelty 4.0 of 10

    A watermark that seeds each token's sampling temperature from a hash of the previous h tokens is claimed to beat the Aaronson watermark under a 30% BERT paraphrase attack, based on a single ROC curve without error bars.

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