The increasing availability of Artificial Intelligence systems in everyday life has drawn growing attention to the right to erasure, as defined by the General Data Protection Regulation. This right raises questions about how learned information can be removed from trained Artificial Intelligence models. In this paper, we examine the problem of machine unlearning from legal, philosophical and technical perspectives. We highlight the misalignment in terminology, as well as between legal expectations, which require data erasure at the individual level, and current technical methods, which typically operate at the class level. In addition, we explore the practical difficulties in evaluating the effectiveness of unlearning, particularly the limitations of using privacy attacks, such as Membership Inference Attack, as evaluation tools. Considering the several challenges in this setting, the lack of standardized metrics for unlearning, and the difficulty of assessing whether a model has truly forgotten specific data, we see the need for developing a unified evaluation framework at the European level. Such a framework should define robust, transparent, and reproducible criteria for assessing unlearning performance.