Abstract
In recent years, it has been increasingly necessary for citizens to understand real life statistical data—an ability that is rarely taught in schools, where the majority of tasks in statistics classes contain fictional data without context and make no demands on students to explore or explain. Since most real-world phenomena are multivariate (See Chap. 2), there is a need to develop students’ abilities dealing with complex data and stories they encounter in the media, in order to help prepare them for informed citizenship. The ProCivicStat project has developed materials to support teaching and learning, in the form of detailed lesson plans; a large repository of resources (http://iase-web.org/islp/pcs/) (in several languages) is freely available. This chapter describes our approach to the development of teaching resources. It introduces our storytelling approach in lesson plans, where we use real data in context to encourage students to explore and understand complex data, produce narrative accounts, and often make recommendations about appropriate social actions. The structure of this chapter is as follows: we start with a brief introduction on problems in most tasks commonly encountered in statistics education, and the need for real data in statistics teaching (Sect. 7.1), followed by the presentation of the milestones that are important for creation of lesson plans (Sect. 7.2), and after that we address the use of real data and our storytelling approach (Sect. 7.3). In Sect. 7.4 we talk briefly about empowering teachers (Sect. 7.4) and describe the teachers’ version of the lesson plan (Sect. 7.5). In Sect. 7.6 we present the guidelines for designing student activities, then proceed with an excerpt of a lesson plan to exemplify products of the proposed guidelines (Sect. 7.7). We then highlight the visualization tools that help promote the data exploration step (Sect. 7.8), and finish with a conclusion (Sect. 7.9). © Springer Nature Switzerl and AG 2022.

Abstract
How might you use data visualisation in your teaching? Here, we offer some ideas, and some provocations to review your teaching. We begin with an invitation to examine some of the historical landmarks in data visualisation (DV), to classify the data presented, and to describe the benefits of a sample of the DV to users. Early uses of DV by Nightingale and Neurath are shown, to provide examples of DV which communicated the need for action, and provoked social change. A number of modern DVs are presented, categorised as: tools to display individual data sets and tools for the exploration of specific rich data sets. We argue that students introduced to the core features of Civic Statistics can acquire skills in all of the facets of Civic Statistics set out in Chap. 3. We conclude by revisiting Herschel, to provoke thoughts about the balance of activities appropriate to statistics courses. © Springer Nature Switzerl and AG 2022.

Abstract
The evolution of markets provides a change in the way organisations act. To improve their competitive performance and stay on the market, organisations often adopt a strategy to establish agreements with other organisations, known as strategic alliances. Several tools, algorithms, and computational systems call upon other sciences as a source of inspiration. In this work we explore flocking behaviour, a paradigm of biology, to analyse the collective intelligence behaviour that emerges from a group of individuals or firms. Inspired by the Cucker and Smale algorithm (C-S), we propose a new version of the flocking algorithm, AllFlock, applied to strategic alliances, considering a learning mechanism. For this new approach, metrics were obtained for the parameters of the C-S algorithm: position, velocity, and influence. The latter uses cooperative games, adapted mechanisms, and methods currently explored in reinforcement learning. We have used Netlogo as the modelling environment. Five parameter configurations were analysed. For each of those configurations, the average number of iterations, the permanence rate of organisations in the alliance, and the average growth of the organisations were computed. The behaviour of the organisations reveals a tendency for convergence, confirming the existence of flocking behaviour. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Abstract Misinformation poses one of the most urgent challenges of our society and raises the question of how to deal with it and manage its rapid spread. To address this problem, a promising approach relies on AI-based misinformation detection. This chapter of the book offers a critical analysis of the ethical implications associated with the design, deployment, and use of misinformation detectors (MDs). Designing and deploying an MD—an AI system that automatically identifies misinformation—is a complex undertaking that requires an interdisciplinary approach, as the challenges faced by MD designers and deployers encompass not only technical aspects, but also linguistic, sociological, political, and especially ethical dimensions. Our analysis is ethics-oriented and follows two main lines of inquiry: (1) Ethics by Design, which focuses on issues related to the design process of an MD, and (2) Ethics of Impact, which addresses the intended and unintended effects of MD deployment and use.