There is an increasing concern that generative AI models may produce outputs that are remarkably similar to the copyrighted input content on which they are trained. This worry has escalated as the quality and complexity of generative models have immensely improved, and the availability of large datasets containing copyrighted material has increased. Researchers are actively exploring strategies to mitigate the risk of producing infringing samples, and a recent line of work suggests to employ techniques such as differential privacy and other forms of algorithmic stability to safeguard copyrighted content. In this work, we examine the question whether algorithmic stability techniques such as differential privacy are suitable to ensure the responsible use of generative models without inadvertently violating copyright laws. We argue that there are fundamental differences between privacy and copyright that should not be overlooked. In particular we highlight that although algorithmic stability may be perceived as a practical tool to detect copying, it does not necessarily equate to copyright protection. Therefore, if it is adopted as standard for copyright infringement, it may undermine copyright law intended purposes.

翻译：随着生成式AI模型的质量和复杂性显著提升，以及包含版权材料的大型数据集的可获取性增加，人们日益担忧这些模型可能生成与其训练所用受版权保护输入内容高度相似的输出。研究人员正积极探索降低侵权样本生成风险的策略，近期一系列工作建议采用差分隐私及其他形式的算法稳定性技术来保护版权内容。本文探讨了差分隐私等算法稳定性技术是否适合确保生成式模型的负责任使用而不会无意中违反版权法。我们认为隐私与版权之间存在不应被忽视的本质差异。特别指出，尽管算法稳定性可能被视为检测抄袭的实用工具，但它并不等同于版权保护。因此，若将其作为版权侵权的判定标准，可能会削弱版权法的立法初衷。