  There is a growing concern that generative AI models will generate outputs
closely resembling the copyrighted materials for which they are trained. This
worry has intensified as the quality and complexity of generative models have
immensely improved, and the availability of extensive datasets containing
copyrighted material has expanded. Researchers are actively exploring
strategies to mitigate the risk of generating infringing samples, with a recent
line of work suggesting to employ techniques such as differential privacy and
other forms of algorithmic stability to provide guarantees on the lack of
infringing copying. In this work, we examine whether such algorithmic stability
techniques are suitable to ensure the responsible use of generative models
without inadvertently violating copyright laws. We argue that while these
techniques aim to verify the presence of identifiable information in datasets,
thus being privacy-oriented, copyright law aims to promote the use of original
works for the benefit of society as a whole, provided that no unlicensed use of
protected expression occurred. These fundamental differences between privacy
and copyright must not be overlooked. In particular, we demonstrate that while
algorithmic stability may be perceived as a practical tool to detect copying,
such copying does not necessarily constitute copyright infringement. Therefore,
if adopted as a standard for detecting an establishing copyright infringement,
algorithmic stability may undermine the intended objectives of copyright law.
