Why the generative AI models do not like the right to be forgotten: a study of proportionality of identified limitations
Why the generative AI models do not like the right to be forgotten: a study of proportionality of identified limitations

Summary/Abstract: The article explores the limitation of one of the privacy and data pro-tection rights when using generative AI models. The identified limitation is as-sessed from the perspective of the ‘essence’ of the right to the protection of per-sonal data. With the further aim of assessing the limitation, the author exploreswhether the right to be forgotten (RTBF) is relevant or effective in an AI/ma-chine learning context. These considerations are focused on the technical prob-lems encountered when applying the strict interpretation of the RTBF. In par-ticular, the antagonism between, on the one hand, the values of privacy and dataprotection rights, and on the other, the technical capabilities of the producer ofthe generative AI models, is further analysed in this context. As the conclusionemphasizes that the RTBF cannot be practicably or effectively exercised in themachine learning models, further considerations of this exposed limitation arepresented. The proportionality principle, as an instrument that supports the prop-er application if there is any limitation of the conflicting rights, has been utilizedto depict the qualitative approach. The integration of this principle supports theconclusion by identifying a more efficient way to address some regulatory is-sues. Hence, the conclusion of the article presents some suggested solutions as tothe interpretation of this right in the light of this new technological advancement.Ultimately, the paper aims to address the legal conundrum of how to balance theconflict between the interest of innovative use of the data (the data producer’sright) and privacy and data protection rights.

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