## 2 Materials and Methods
### Previous Work on DPIA
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Requirement** & **DPIA** & **FRIA** \\ \hline \multirow{4}{*}{Mitigation Measures} & Technical and organizational measures (e.g., encryption, pseudonymization, access controls). & Ethical, legal, and technical safeguards, including safeguards related to the inners of the AI system (e.g., bias detection, fairness audits, redress mechanisms, and explainability). & Ethical, legal, and technical safeguards, including safeguards related to the inners of the AI system (e.g., bias detection, fairness audits, redress mechanisms, and explainability). & Ethical, legal, and technical safeguards, including safeguards related to the inners of the AI system (e.g., bias detection, fairness audits, redress mechanisms, and explainability). & Ethical, legal, and technical safeguards, including safeguards related to the inners of the AI system (e.g., bias detection, fairness audits, redress mechanisms, and explainability). \\ \hline \multirow{2}{*}{Time Period and Frequency} & \multirow{2}{*}{Expected duration of processing.} & A description of the intended timeframe and usage frequency for the high-risk AI system. \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._algorithms. [PERSON] and [PERSON] [36] suggest that future interpretations and implementations of the GDPR should place greater emphasis on the systemic governance aspects of algorithmic accountability. While DPIAs help improve understanding of data processing activities, promote transparency, and build trust with data subjects, they also present challenges. They can be resource-intensive and, at times, require subjective judgment in risk assessment. There are gaps in the DPIA related to ADM systems including a lack of guidelines on evaluating the complex risks associated with ADM, insufficient focus on understanding the long-term and indirect effects on individual's rights, as well as challenges in engaging stakeholders and ensuring public transparency [36; 37]. Moreover, there is often a need for more robust mechanisms to ensure accountability and take corrective actions when biases or harms are detected. These gaps underscore the importance of comprehensive frameworks that specifically address the challenges posed by ADM in safeguarding the rights of data subjects. For the specific case of AI systems, implementing DPIAs presents additional difficulties due to the nature of these technologies. One significant obstacle is the inherent complexity of AI systems, known as \"black boxes\", rendering risk assessment challenging due to the fact that their internal processes are not fully transparent. Furthermore, AI's reliance on large datasets containing probably sensitive personal information raises significant privacy concerns. Moreover, the dynamic nature of AI means that an assessment conducted at one point in time may become outdated due to continuous learning and adaptation that can alter the function of the system. The general guidelines provided by the GDPR for DPIAs do not offer the required accuracy to effectively address the unique characteristics of AI systems. This gap presents a challenge in balancing the drive for technological progress and the imperative need for privacy and data protection [38]. A policy brief published by the Brussels Laboratory for Data Protection and Privacy Impact Assessments (d.pia.lab) [39], provides an in-depth analysis and recommendations for the EU to enhance the requirement for DPIA. The brief highlights the benefits of impact assessments, including informed decision-making, protection of societal concerns, anticipatory thinking, compliance assistance, and enhancement of public trust. Despite their benefits, impact assessments face criticism for adding bureaucracy, and complexity, and being perceived as a formality rather than a meaningful analysis.