Sónia Carvalho Teixeira

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

Abstract
Citizens are more and more encouraged to participate in public policy decision processes and, therefore, critical questions regarding our lives are asked every day. Informed citizens need access to data, and knowledge in order to explore, understand, and reason about information of a multivariate nature; it is not obvious how to access such data, or how to work with them. Educators face the challenge of adopting new approaches, and grasping new opportunities in order to support the development of students into informed citizens as adults. Educators often do not have time to locate information sources; moreover, it is a challenge to exploit the possibilities of open data wisely. This chapter points to data sets we have found valuable in teaching Civic Statistics; data must be authentic, and reflect the complexities of data used to inform decision making about social issues (whose features are explained in Chap. 2). Topics include refugees, malnutrition, and climate change. We provide enough details so teachers can locate and employ these data sets, or similar ones, as part of regular instruction. Information is made accessible using the innovative tool CivicStatMap, developed to provide access to teaching materials, along with data and analysis tools, including tools to support data visualisation.

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
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government’s and its citizens’ relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp_mi, smc_?2 and mc_?2 conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.