We are living in a time when many teachers say they are feeling burnt out, and many others have left the profession altogether. Even new teachers who might start out feeling enthusiastic are likely to leave the profession after a few years. Teachers say the pressures they feel don’t match their view of what teaching is supposed to be all about – caring for, and teaching, children and young people. So, what do teachers do? What does the public (and, for that matter, Hollywood movie producers) think teachers do? This chapter argues that we have a bit of a mismatch between what people outside the profession think, and the experiences of teachers themselves. It also argues that broader changes in education, such as the use of data to govern teachers’ work has created extra pressure on teachers.

There are all sorts of dilemmas when it comes to technology and education. How much should be allowed in schools? Do teachers have to worry about students’ data security and privacy? Is it ok for you to ask a computer to write your essay for you? Are we ruining the eyesight and attention spans of an entire generation thanks to excessive screen time? This chapter looks at the debates that exist when it comes to digital technology and education. It will be argued here that the interplay between technology and education is highly complex – and changing – at a pace that is almost unimaginable.

As business transactions and the global economy become increasingly digitalized, international investment disputes will deal with novel assets in new boundary-defiant contexts. Indeed, jurisdictional arguments and objections will likely require arbitral tribunals to confront with the uneasy task of delineating the ‘localization’ of investments in digital economy assets such as cryptocurrency, non-fungible tokens, and data-related investments. However, given that even more traditional assets have raised a variety of problems relating to territorial nexus and localization, the authors believe that the digital economy emphasizes what are essentially differences in degree rather than in kind. This chapter discusses the complexities that arise in considering the idiosyncrasies of investments in digital economy assets within a traditional territorially defined jurisdictional framework. First, the authors present some of those new digital economy assets and canvass several typical cross-border challenges inherent in international investment arbitration. Second, they question how traditional objections to jurisdiction ratione personae and jurisdiction ratione materiae might be employed when the investments in question relate to those digital developments. Third, the chapter raises questions about states’ jurisdiction to prescribe, and ponders the potential effects for purposes of jurisdiction of states asserting their authority to prescribe over investments or investors outside their territory.

Chapter 7 considers the developments that have taken place since the beginning of the von der Leyen II Commission, identifying how there has not only been continuity in the EU’s approach to technology control and its links to digital sovereignty but also an expansion and reinforcement of the approach. Faced with increased instability and geopolitical threat, the linkage of security and economy has become even more explicit for the von der Leyen II Commission, with the Competitiveness Compass taking an approach that appears to be a more assertive form of regulatory mercantilism, in which the element of defence is specifically incorporated into the EU’s rationale for action, with an expansion of technology controls including the development of an explicit push for defence technology industrial policy, the increased control over external dependencies and supply chains through its Preparedness Strategy, and an AI policy for Europe that includes significant investments for AI gigafactories.

Chapter 1 introduces basic terminology. Terms such as artificial intelligence, data, algorithm, machine learning, neural networks, deep learning, large language models, generative AI and symbolic AI are presented to develop a sense of what AI is, how it has evolved, and what it does. This chapter also introduces some of the major conceptual disagreements in the field. Different ideas about how to develop AI in the best way drive disagreements, as well as philosophical differences over what intelligence means and whether machines can develop human-like intelligence.

Chapter 6 considers the Commission’s intent to reduce critical dependencies on third-country-based data servers and computing capabilities. It explores the Brussels effect as a motivator for further action on exporting regulatory norms concerning the protection of personal data and non-personal data, coupled with concerns over lack of competitiveness in data-derived commercial activity, particularly in the field of AI. Concerns over competitiveness impacting Europe’s security, and the risks posed by unauthorised access to industrial or sensitive data from the governments in third countries, the Commission desires increased regulatory control. This is facilitated by industrial policy aimed at both promoting European data server infrastructure and encouraging switching to those Common European Data Spaces as a means of building up a European data economy, while placing strict limitations on the export of non-personal data outside of Europe’s borders. The Commission combines this with an attempt to utilise the Brussels effect to ensure that European standards concerning the safe use of AI technologies become global standards shaped by the EU and its values.

This chapter explores the fundamentals of data in data science, covering data types (structured vs. unstructured), collection sources (open data, social media APIs, multimodal data, synthetic data), and storage formats (CSV, TSV, XML, RSS, JSON). It emphasizes the critical importance of data pre-processing, including data cleaning (handling missing values, smoothing noisy data, data munging), integration, transformation, reduction, and discretization. Through hands-on examples, the chapter demonstrates how to systematically prepare "dirty" real-world data for analysis by addressing inconsistencies, outliers, and missing information. The chapter highlights that data preparation is often half the battle in data science, requiring both technical skills and careful attention to data quality and bias.

This introductory chapter defines data science as a field focused on collecting, storing, and processing data to derive meaningful insights for decision-making. It explores data science applications across diverse sectors including finance, healthcare, politics, public policy, urban planning, education, and libraries. The chapter examines how data science relates to statistics, computer science, engineering, business analytics, and information science, while introducing computational thinking as a fundamental skill. It discusses the explosive growth of data (the 3Vs: velocity, volume, variety) and essential skills for data scientists, including statistical knowledge, programming abilities, and data literacy. The chapter concludes by addressing critical ethical concerns around privacy, bias, and fairness in data science practice.

This chapter explores the fundamentals of data in data science, covering data types (structured vs. unstructured), collection sources (open data, social media APIs, multimodal data, synthetic data), and storage formats (CSV, TSV, XML, RSS, JSON). It emphasizes the critical importance of data pre-processing, including data cleaning (handling missing values, smoothing noisy data, data munging), integration, transformation, reduction, and discretization. Through hands-on examples, the chapter demonstrates how to systematically prepare "dirty" real-world data for analysis by addressing inconsistencies, outliers, and missing information. The chapter highlights that data preparation is often half the battle in data science, requiring both technical skills and careful attention to data quality and bias.

The Communist Party is building a digitally capable state to remain at the vanguard of social and political development in China. CPC leaders are advancing digitalization forcefully throughout the governance and economic system through various policy initiatives. Informatization serves both better governance and public service, by overcoming previous bottlenecks and spatial challenges in administration. It also enhances the party state’s surveillance and monitoring capabilities. The CPC’s goals for China’s future under its continued rule require a digital infrastructure and economy that is both efficacious and subject to the Party’s control. To this end, the CPC is building out a comprehensive governance architecture for cyberspace, including the world’s most expansive data regulatory regime. The current leadership regards these digital capabilities as a key part of its comprehensive governance model that will enable the CPC to implement its domestic and international vision for China in the coming years.

This introductory chapter defines data science as a field focused on collecting, storing, and processing data to derive meaningful insights for decision-making. It explores data science applications across diverse sectors including finance, healthcare, politics, public policy, urban planning, education, and libraries. The chapter examines how data science relates to statistics, computer science, engineering, business analytics, and information science, while introducing computational thinking as a fundamental skill. It discusses the explosive growth of data (the 3Vs: velocity, volume, variety) and essential skills for data scientists, including statistical knowledge, programming abilities, and data literacy. The chapter concludes by addressing critical ethical concerns around privacy, bias, and fairness in data science practice.

This chapter adds three principal observations to the theory of singular compositional explanation. These observations are essential to locating the theory of singular compositional explanation to the context of scientific experimental work. The first observation is that scientists sometimes explain the rates of activity instances. One way they explain them is in terms of the number of lower level individuals engaged in activity instances. The second is that scientists often use singular compositional abductive explanations in explaining experimental results. Third, scientists often use singular compositional abductive explanations in the context of controlled experiments.

This chapter challenges the conventional wisdom of how users of social media platforms such as Instagram, X, or TikTok pay for service access. It argues that rather than merely exchanging data for services, users unknowingly barter their attention, emotions, and cognitive resources – mental goods that corporations exploit through technologically managed systems like targeted advertising and habit-forming design. The chapter explores how these transactions are facilitated not by legal contracts but by code, which allows social media companies to extract value in ways that traditional legal conceptual frameworks cannot capture. It further highlights the negative externalities of these exchanges, such as cognitive impairments and mental health issues, framing them as pollution byproducts of the attention economy. By examining both the visible and hidden dimensions of this technologically mediated exchange, the chapter calls for a deeper understanding of the mechanisms that govern our interactions with digital platforms rather than rushing to propose new legal solutions.