This chapter provides an introductory overview of the recent emergence of facial recognition technologies (FRTs) into everyday societal contexts and settings. It provides valuable social, political, and economic context to the legal, ethical, and regulatory issues that surround this fast-growing area of technology development. In particular, the chapter considers a range of emerging ‘pro-social’ applications of FRT that have begun to be introduced across various societal domains - from the application of FRTs in retail and entertainment, through to the growing prevalence of one-to-one ID matching for intimate practices such as unlocking personal devices. In contrast to this seemingly steady acceptance of FRT in everyday life, the chapter makes a case for continuing to pay renewed attention to the everyday harms of these technologies in situ. The chapter argues that FRT remains a technology that should not be considered a benign addition to the current digital landscape. It is technology that requires continued critical attention from scholars working in the social, cultural, and legal domains.

State actors in Europe, in particular security authorities, are increasingly deploying biometric methods such as facial recognition for different purposes, especially in law enforcement, despite a lack of independent validation of the promised benefits to public safety and security. Although some rules such as the General Data Protection Regulation and the Law Enforcement Directive are in force, a concrete legal framework addressing the use of facial recognition technology (FRT) in Europe does not exist so far. Given the fact that FRT is processing extremely sensitive personal data, does not always work reliably, and is associated with risks of unfair discrimination, a general ban on any use of artificial intelligence for automated recognition of human features at least in publicly accessible spaces has been demanded. Against this background, the chapter adopts a fundamental rights perspective, and examines whether and to what extent a government use of FRT can be accepted under European law.

This chapter establishes an explicit link between foreign aid inflows and development indicators classified in the multidimensional setting of the SDGs. This linkage is not a black box as it takes advantage of the model’s causal chains describing budget allocations and indicator performance. First, we create counterfactuals by removing aid flows. Hence, we can estimate aid impacts and assess their statistical significance at the indicator or country levels during the first decade of the 21st century. Second, we produce a validation exercise comparing our results with econometric evidence found in a well-known sector-level study (access and sanitation of water) using a subset of our data.

This chapter introduces a model in which a government allocates financial resources across several policy issues (development dimensions), and a set of public servants (or agencies) that, through government programmes, transform public spending into policy outcomes. We start by describing the macro-level dynamics and the relevant equations involved. Then, we introduce a political economy game between the government and its officials (or public servants). First, we describe the public servants’ decision making in an environment of uncertainty through reinforcement learning. Second, we elaborate on the problem of the government (or central authority) and how we can specify its heuristic strategy. Finally, we provide an overview of the entire structure of the model.

This chapter elaborates on the calibration and validation procedures for the model. First, we describe our calibration strategy in which a customised optimisation algorithm makes use of a multi-objective function, preventing the loss of indicator-specific error information. Second, we externally validate our model by replicating two well-known statistical patterns: (1) the skewed distribution of budgetary changes and (2) the negative relationship between development and corruption. Third, we internally validate the model by showing that public servants who receive more positive spillovers tend to be less efficient. Fourth, we analyse the statistical behaviour of the model through different tests: validity of synthetic counterfactuals, parameter recovery, overfitting, and time equivalence. Finally, we make a brief reference to the literature on estimating SDG networks.

This chapter introduces the reader to the problem of policy prioritisation and why quantitative/computational analytic frameworks are much needed. We explain the various academic- and policy-oriented motivations for developing the Policy Priority Inference research programme. We apply this computational framework in the study of the SDGs and the feasibility of the 2030 Agenda of sustainable development.

This chapter formulates an analytical toolkit that incorporates an intricate – yet realistic – chain of causal mechanisms to explain the expenditure–development relationship. First, we explain several reasons why we take a complexity perspective for modelling the expenditure–development link and why we choose agent-based modelling as a suitable tool for assessing policy impacts in sustainable development. Second, we introduce the concept of social mechanisms and explain how we apply them to measure the impact of budgetary allocations when systemic effects are relevant. Third, we compare different concepts of causality and explain the advantages of an account that simulates counterfactual scenarios where policy interventions are absent.

This chapter provides a comprehensive framework to understand and quantify structural bottlenecks in a setting of multidimensional sustainable development. First, we formalise the idea of an idiosyncratic bottleneck when thinking in a hypothetical situation where a government has all the necessary resources to guarantee the success of its existing programmes (i.e., the budgetary frontier). Second, we compare the development gaps between the baseline and counterfactual outputs to assess how sensitive are the different indicators when they operate at the budgetary frontier. Third, we combine this information with the historical performance of indicators to develop a methodology that identifies idiosyncratic bottlenecks. Finally, we elaborate on a flagging system to differentiate between idiosyncratic bottlenecks according to the ‘urgency’ to unblock them.