Abstract
This work explores network science to understand and visualize the intricate interconnectivity within organizations. The age of big data emphasizes the importance of deriving new insights by transforming data into networks to study their connections. The document introduces a three-step maturity framework for navigating network science, starting with the basics of network construction, moving on to standard metrics, and culminating in an examination of network topology and dynamics. The author aims to clarify the subject and encourage further exploration, suggesting that while network science may not have all the answers, it offers a critical analytical framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Over the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the 'black box' and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-and-effect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.

Abstract
In collaborative learning, evaluating the process involves teamwork dynamics, and assessing the product focuses on the accuracy and quality of the final output. Assessment plays a crucial role, as it defines and measures the effectiveness of group activities to ensure that learning objectives are met. Mediation analysis is an important technique to better understand relationships between variables, specifically designed to test hypotheses about potential causal effects in various areas. However, many research initiatives have been discontinued prematurely due to the Baron-Kenny data restrictions. This research takes a case study of online learning from the Portuguese Open University to determine if and how group selection and interaction frequency affect individual assessment. The contribution lies in applying quantitative causal mediation analysis to collaborative learning assessment. The Lambda Mediation Ratio is proposed to enhance mediation analysis by enabling quick and flexible categorization into full, partial, or no mediation. Using Moodle platform logs and student outcomes, it was possible to find a significant influence of group dynamics on academic performance, highlighting the practical application of this improved methodology in an educational context. These findings reassure us of the relevance and applicability of this research in real-world educational settings. © 2024 Universidad de Murcia. All rights reserved.

Abstract
In this article we extend the Marketing Mix Diffusion (MMD) model to inhomogenous networks (i.e. complex networks of arbitrary topology). The (Homogenous) MMD model is an innovation diffusion model, similar to the Bass model, which includes four decision variables (the 4Ps of Marketing: Product, Price, Place, Promotion). We introduce the Inhomogenous MMD (IMMD) model and we conduct two separate experiments: one based on simulation and another one relying on empirical evidence. The simulation study compares the behavior of the IMMD model with the classic Bass diffusion model. Results suggest that the classic Bass model is able to represent the IMMD curves quite well in most cases. The IMMD is more general and capable of representing extreme scenarios. The empirical study focuses on the geographic diffusion of mobile broadband technology in Japan, combining adoption data with a spatial network of municipalities. The in-sample performance of the model is comparable to the existing methods, which suggests a good explanatory power of the IMMD model.

Abstract
Data maturity models are an important and current topic since they allow organizations to plan their medium and long-term goals. However, most maturity models do not follow what is done in digital technologies regarding experimentation. Data Science appears in the literature related to Business Intelligence (BI) and Business Analytics (BA). This work presents a new data science maturity model that combines previous ones with the emerging Business Experimentation (BE) and causality concepts. In this work, each level is identified with a specific function. For each level, the techniques are introduced and associated with meaningful wh-questions. We demonstrate the maturity model by presenting two case studies.

Abstract
The growing use of machine learning for integrity assessments in public administration has intensified interest in understanding how algorithms can detect corruption risk-a topic of increasing relevance in the context of rising demands for transparency. Previous research on fraud detection often overlooks the dual challenge of extreme class imbalance and the need for model explainability. This study addresses both issues by combining data-level and algorithm-level techniques in a real-world dataset from Brazil's Federal District, where there is one corruption case for every 707 non-corruption cases (a ratio of 1:707). Data engineering was essential, encompassing gathering, cleaning, transformation, and dimensionality reduction to enhance model performance and interpretability. Among the tested models, weighted logistic regression stood out, achieving the best AUC (0.692). To increase transparency, we employed SHapley Additive exPlanations, enabling both global and local interpretability of predictions. The analysis identified strong predictors of corruption risk, such as business ownership, political candidacy, and frequent job function changes. This work provides a replicable pipeline that integrates imbalanced learning and explainable AI, offering valuable contributions to risk management and decision-making in the public sector.