Speaker
Description
While artificial intelligence (AI) has brought transformative benefits across numerous domains, it has also raised serious concerns about privacy and personal data management. A prominent example is the use of publicly shared images, which can be repurposed for facial recognition and potentially lead to unwanted surveillance. In response, regulations such as the General Data Protection Regulation (GDPR) have introduced provisions like the "right to be forgotten" to give individuals greater control over their data. However, AI models' capacity to memorize training data poses significant challenges in complying with such regulations, as models can inadvertently reveal details about the original data. Addressing this issue requires not only deleting the designated data but also removing any learned representations derived from it. This need has led to the emergence of machine unlearning, a field focused on removing the influence of specific data samples from trained AI models. In this context, we present our latest results advancing machine unlearning techniques to better support data privacy and regulatory compliance.
