# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 2 Related Work  
It is essential to focus on data management aspects when AI systems are designed. Firstly, it is mandatory to fulfill the data privacy regulations of the country or region where the data are managed when a system is handling sensitive personal data. An example can be found in the European Union with the General Data Protection Regulation (GDPR) [26], but most countries have their own legislation about data privacy management. Secondly, it is also essential to consider a data policy. However, data policies have not been standardized, and there is not a unique policy to apply. If we focus on higher education institutions, each organization is applying its own policy on the basis of the challenges they faced during the analysis of the data it possesses [27; 28; 29]. Nevertheless, data policies are not enough [30] when we face predictive analytics infrastructure built upon AI requirements. AI needs to consider more requirements because data are not only used for analyzing and visualizing information. Data are also used for impacting people's decisions and behavior. Thus, ethical issues appear as a new relevant dimension. Governments and institutions are investing efforts to define guidelines to develop TAI systems and standards that should positively impact citizens [13; 14; 15; 16; 17; 18]. Although there are different definitions, recommendations, challenges, guidelines, and standardization procedures, all are working on similar concepts to define the requirements to be analyzed. The TAI system concept has been studied in several works [31; 32; 33; 34]. The authors of [35] (p. 5) define a TAI system as \"a system that is developed, deployed, and used in ways that not only ensure its compliance with all relevant laws and its robustness but especially its adherence to general ethical requirements.\" The general ethical requirements include inclusive growth, sustainable development, and well-being; human-centered values and fairness; transparency and explainability; robustness, security, and safety; and accountability [36]. The authors of [37] (p. 29) proposed that \"in order to make it trustworthy, computer scientists need to consider measures..., robustness to data set shift, robustness to poisoning, fairness, interpretability, end-to-end service-level provenance and transparency, and application for social good\". In other words, a \"TAI system should provide benefence, non-maleficence, autonomy, justice, and explicability\" [38] (p. 696). In all these definitions, the requirements can be reduced to a single word: trust. Stakeholders should trust the system, and trustworthiness is accomplished by understanding and making explicit system capabilities explicit. Without knowing all the features, stakeholders will have concerns about feeling completely confident with the system. Note that visual interface design is a key issue to improve confidence. For instance, the author in [39] identified emotional and usability criteria factors that directly impacted interface acceptance. Thus, a good user interface design could lead to a greater confidence level. This effect is even more critical in education. Teachers guide learners, and they tend to fully control all the learning process, from the materials and contents to the activities and feedback provided to the learners. Introducing in the learning process an instructional technology with AI involves a new element in the equation. Learners may discover adverse effects on personalization by feeling that the system is not treating all learners equally [40], and teachers may have doubts about the applicability value of AI. However, there are clear positive effects on the utilization of TAI systems in education. The authors of [41] (p. 3) describe the positive benefits: \"personalized learning, the support of learners with special needs,... reduce dropout, and assessing new skillsets.\" Moreover, TAI systems can contribute to the learning design research field [42] by producing technological tools that are easy to share and reuse [43].