# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 5 Results and Discussion
### Privacy and Data Governance  
Privacy is also a critical issue in AI systems [49], and it is intimately related to data governance. Privacy on the stored data or the extracted knowledge must be taken into account. The infrastructure handles data anonymously to impersonate the data and the obtained knowledge from learners. Thus, the learner's identity is unknown in any place of the infrastructure. Additionally, the anonymization is provided by a secured service of the university, leveraging the responsibility of the infrastructure to manage the complete anonymization process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Semester** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline _ACC_ & 77.56 & 80.86 & 83.72 & 86.20 & 88.43 & 90.46 & 92.39 & 93.99 & 95.25 \\ \hline _TNR_ & 84.79 & 86.30 & 87.78 & 89.30 & 90.95 & 92.46 & 94.04 & 95.54 & 96.91 \\ \hline _TPR_ & 55.71 & 64.51 & 71.70 & 77.40 & 81.88 & 85.42 & 88.53 & 90.47 & 91.22 \\ \hline F1.5 & 53.50 & 61.00 & 67.50 & 73.10 & 77.97 & 81.75 & 85.36 & 88.14 & 90.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: LOESS regression of the PGAR model for _ACC_, _TNR_, _TPR_, and _F1.5_ metrics.  
Figure 9: Scatter plots of the PGAR model for (**a**) accuracy; (**b**) _TNR_; and (**c**) _TPR_, _F1.5.  
The system aims to exclusively store the data needed to train the models. This is achieved by specifying the appropriate fields to be captured in the ETL process on the basis of the needed ones in the predictive models. Since selecting the minimal set of fields to train a model is not an easy task, the computational service provides a tool to minimize the number of features using the Feature Selection technique [86]. Such a technique helps to identify superfluous features already covered by other fields captured in the model.  
Data governance, which is related to privacy, manages the availability, usability, integrity, and security of sensitive data. UOC has its own data policy [87], and the EWS adheres to such policy following the European GDPR [26]. Additionally, the university has a Research Ethical Committee that assesses that the utilization of learners' data meets ethical codes and current privacy legislation. The system should protect the data's ownership, the data to be displayed, and who has access to which kind of data. The system meets the privacy policy by asking users to consent before utilizing their data, informing them of used data and the objective of data processing, and securing access to information by roles. In the designed EWS, the data's owner is the learner and, thus, they have the right to consent to their utilization, as well as to revoke it. Note that the data visualization is restricted by the user's roles described in Section 4.1.3. Although the ALTAI self-assessment guidelines recommend aligning the system's data governance with other AI-related standards [15; 18], it is currently out of the scope of this paper. However, it will be done as future work in the following design cycle of the system.