With the increased availability of data and the capacity to make sense of these data, computational approaches to analyze, model and simulate public policy evolved toward viable instruments to deliberate, plan, and evaluate them in different areas of application. Such examples include infrastructure, mobility, monetary, or austerity policies, policies on different aspects of societies (health, pandemic, skills, inclusion, etc.). Technological advances along with the evolution of theoretical models and frameworks open valuable opportunities, while at the same time, posing new challenges. The paper investigates the current state of research in the domain and aims at identifying the most pressing areas for future research. This is done through both literature research of policy modeling and the analysis of research and innovation projects that either focus on policy modeling or involve it as a significant component of the research design. In the paper, 16 recent projects involving the keyword policy modeling were analyzed. The majority of projects concern the application of policy modeling to a specific domain or area of interest, while several projects tackled the cross-cutting topics (risk and crisis management). The detailed analysis of the projects led to topics of future research in the domain of policy modeling. Most prominent future research topics in policy modeling include stakeholder involvement approaches, applicability of research results, handling complexity of models, integration of models from different modeling and simulation paradigms and approaches, visualization of simulation results, real-time data processing, and scalability. These aspects require further research to appropriately contribute to further advance the field.

People rely extensively on online social networks (OSNs) in Africa, which aroused cyber attackers’ attention for various nefarious actions. This global trend has not spared African online communities, where the proliferation of OSNs has provided new opportunities and challenges. In Africa, as in many other regions, a burgeoning black-market industry has emerged, specializing in the creation and sale of fake accounts to serve various purposes, both malicious and deceptive. This paper aims to build a set of machine-learning models through feature selection algorithms to predict the fake account, increase performance, and reduce costs. The suggested approach is based on input data made up of features that describe the profiles being investigated. Our findings offer a thorough comparison of various algorithms. Furthermore, compared to machine learning without feature selection and Boruta, machine learning employing the suggested genetic algorithm-based feature selection offers a clear runtime advantage. The final prediction model achieves AUC values between 90% and 99.6%. The findings showed that the model based on the features chosen by the GA algorithm provides a reasonable prediction quality with a small number of input variables, less than 31% of the entire feature space, and therefore permits the accurate separation of fake from real users. Our results demonstrate exceptional predictive accuracy with a significant reduction in input variables using the genetic algorithm, reaffirming the effectiveness of our approach.

Border artificial intelligence (AI)—biometrics-based AI systems used in border control contexts—proliferates as common tools in border securitization projects. Such systems classify some migrants as posing risks like identity fraud, other forms of criminality, or terrorism. From a human rights perspective, using such risk framings for algorithmically facilitated evaluations of migrants’ biometrics systematically calls into question whether these kinds of systems can be built to be trustworthy for migrants. This article provides a thought experiment; we use a bottom-up responsible design lens—the guidance-ethics approach—to evaluate if responsible, trustworthy Border AI might constitute an oxymoron. The proposed European AI Act only limits the use of Border AI systems by classifying such systems as high risk. In parallel with these AI regulatory developments, large-scale civic movements have emerged throughout Europe to ban the use of facial recognition technologies in public spaces to defend EU citizens’ privacy. The fact that such systems remain acceptable for states’ usage to evaluate migrants, we argue, insufficiently protects migrants’ lives. In part, we argue that this is due to regulations and ethical frameworks being top-down and technology driven by focusing more on the safety of AI systems than on the safety of migrants. We conclude that bordering technologies developed from a responsible design angle would entail the development of entirely different technologies. These would refrain from harmful sorting based on biometric identifications but would start from the premise that migration is not a societal problem.