This chapter assesses the potential of technological tools to ensure voluntary compliance without coercion and improve the predictability of trustworthiness, focusing on the ethical challenges such differentiation might create.

Critics from across the political spectrum attack social media platforms for invading personal privacy. Social media firms famously suck in huge amounts of information about individuals who use their services (and sometimes others as well), and then monetize this data, primarily by selling targeted advertising. Many privacy advocates object to the very collection and use of this personal data by platforms, even if not shared with third parties. In addition, there is the ongoing (and reasonable) concern that the very existence of Big Data creates a risk of leaks. Further, aside from the problem of Big Data, the very existence of social media enables private individuals to invade the privacy of others by widely disseminating personal information. That social media firms’ business practices compromise privacy cannot be seriously doubted. But it is also true that Big Data lies at the heart of social media firms’ business models, permitting them to provide users with free services in exchange for data which they can monetize via targeted advertising. So unless regulators want to take free services away, they must tread cautiously in regulating privacy.

The area where social media has undoubtedly been most actively regulated is in their data and privacy practices. While no serious critic has proposed a flat ban on data collection and use (since that would destroy the algorithms that drive social media), a number of important jurisdictions including the European Union and California have imposed important restrictions on how websites (including social media) collect, process, and disclose data. Some privacy regulations are clearly justified, but insofar as data privacy laws become so strict as to threaten advertising-driven business models, the result will be that social media (and search and many other basic internet features) will stop being free, to the detriment of most users. In addition, privacy laws (and related rules such as the “right to be forgotten”) by definition restrict the flow of information, and so burden free expression. Sometimes that burden is justified, but especially when applied to information about public figures, suppressing unfavorable information undermines democracy. The chapter concludes by arguing that one area where stricter regulation is needed is protecting children’s data.

In the previous chapters, we built the basic foundation of satellite remote sensing. In this chapter we will explore a relatively recent innovation in information technology called cloud computing that has dramatically improved data accessibility and the practicality of applying large satellite remote sensing datasets for water management. Future chapters on specific targets and water management themes will have hands-on examples and assignments based on actual satellite data. Most of these chapters will assume prior knowledge of cloud computing for understanding and completing assignments. Since cloud computing is gradually proliferating in all walks of water management practice, the aim of this chapter is to introduce readers to cloud computing concepts and specific tools currently available for dealing with the very large satellite data sets on water.

This is the first chapter of the book. The goal of this chapter is to introduce ourselves to the growing importance of using satellite remote sensing to manage our water. We will try to understand this in the context of the underlying challenges and new global forces shaping up this century that are expected to make traditional ways of managing water using in-situ data more challenging.

Since the 1990s, big data has rapidly grown, influencing business, government, and healthcare. Fueled by networked devices, social media, and affordable cloud storage, it features voluminous datasets with diverse types, rapid updates, and accuracy concerns. Applications span retail, manufacturing, transportation, finance, and education, yielding benefits like data-driven decisions, optimization, personalized marketing, scientific progress, and fraud detection. However, challenges arise from complexity, necessitating interdisciplinary collaboration, privacy issues, potential cyberattacks, and the need for robust data protections. Accurate interpretation is crucial, given the risk of costly misinterpretations. Moreover, significant resources for storage, processing, and analysis raise environmental concerns, while legal and ethical considerations add complexity. Overreliance on data may lead to missed opportunities, underscoring the importance of balancing insights with human judgment. In conclusion, big data offers immense potential but poses significant challenges. Navigating this landscape requires a nuanced understanding, fostering responsible data practices to unlock its potential for informed decision-making and advancements across diverse fields.

This chapter addresses how one could quantify and explore the impact of geopolitics on global businesses. Computational geopolitics is an attempt to integrate quantitative methods and geopolitical analysis to understand and predict trends. The explosive growth of data, improvements in computational power, and access to cloud computing have led to a proliferation of computational methods in analyzing geopolitics and its impact on companies. The chapter explores some tools and techniques used in computational geopolitics, including events-based approaches to measuring geopolitical tensions, textual approaches, and empirical approaches. In addition, it provides examples of ways in which analysts can quantify the impact of geopolitics on trade and foreign direct investment. It also introduces experimental methods to assess the effectiveness of companies’ strategic responses to geopolitical tensions. Large language models (LLMs) can be used for sentiment analysis, spotting trends, scenario building, risk assessment, and strategic recommendations. While they methods offer advances in quantifying the impact of geopolitics on global businesses, analysts should also be cautious about data quality and availability as well as the complexity of the phenomenon and the geopolitics of AI. The chapter concludes by pointing the reader to some widely used data sources for computational geopolitics.

A distinction between types of methods (understanding and explanation) that generate different kinds of evidence relevant to the psychiatric assessment is characterised. The distinction is animated with both non-clinical and clinical examples and exercises. Scepticism about the distinction is addressed, and three influential systems of psychiatric knowledge which collapse understanding and explanation in different ways are discussed. The argument is made that the distinction (analogous to the romantic/classic distinction) resurfaces and is compelling. However, another challenge becomes important – holism in psychiatric assessment – which the understanding/explanation distinction leaves in an unsatisfactory state.