## 7 Conclusions  
The analysis in this paper indicates how challenging it is to ensure that personal data protection principles, as provisioned in the GDPR, are in place when ML techniques are used. Indeed, we focused on DP as a well-known privacy-enhancing technique to alleviate relevant threats, and we confirmed, through an extensive set of experiments, that there is no guarantee that all the desired goals (i.e., the accuracy of the algorithm's output while protecting personal data in the training dataset) can be achieved. Therefore, there exist challenges related to the facts that (i) no privacy-enhancing technique should be considered a panacea with respect to mitigating the relative risks stemming from the process of model training, and (ii) the application of a privacy-enhancing technique may also affect the overall accuracy of the model. This, in turn, in decision-making systems, also has an impact on fundamental rights, including the right to personal data protection. Hence, the experimental results in Section 6 further reveal the inherent difficulties in striking a proper balance between privacy and accuracy. Therefore, a very careful assessment of the actual data protection risks is needed for each specific case. As a generic conclusion, it can be considered that the European legal provisions, which (try to) embed safeguards, such as impact assessments before the start of any data processing, could be considered a \"nice model\" globally for establishing appropriate legal frameworks, whilst, especially the requirement for human intervention in automated decision-making systems should be considered a prerequisite for respecting fundamental human rights. Indeed, with respect, e.g., to personal data protection, adopting the data protection by design principle--although it has its own challenges in terms of its proper implementation--seems to be the proper way to alleviate data protection issues from the very beginning of the process since posteriori privacy-enhancing solutions may not be sufficient. On the other side, it is also essential that the different communities, i.e., researchers, AI systems developers, AI systems providers, AI systems users (i.e., data controllers in terms of the GDPR's provisions), and data protection authorities, should be in a close relationship to eliminating any possible lack of understanding between them. Especially the outcomes and methodologies from the research community need to be clearly identified in the context of an impact assessment. More specifically, stakeholders that are to conduct (either jointly or alone) an impact assessment should be fully aware of the relevant State-of-the-Art research outcomes on data protection engineering in order to justify any decision that is to be made with respect to the evaluation of the remaining risks. All these constitute, in turn, new challenges that need to be addressed. In a recent report [38], governance aspects in terms of the risk management process for AI systems are elaborated and described as the key to achieving trustworthy AI. Governance is described, therein, as an activity with two elements: the first concerns the governance of the risk management process itself and includes monitoring, reviewing, documenting, communicating, and consulting on the process and its outcomes, whilst the second ensures the effectiveness of the risk management process by embedding it in the culture and broader governance processes of organizations. Based on this, as well as on the discussion and findings in this paper, it is evident that the aforementioned governance procedures should incorporate considerations for employing efficient data protection engineering in AI systems, which--as stated above--entails the active involvement and collaboration of several stakeholders, including the research community and academia. Clearly, data protection engineering should not be considered an obstacle to ensuring XAI, which is also important for accountability.