This chapter discusses the current state of laws regulating facial recognition technology (FRT) in the United States. The stage is set for the discussion with a presentation of some of the unique aspects of regulation in the United States and of the relevant technology. The current status of FRT regulation in the United States is then discussed, including general laws (such as those that regulate the use of biometrics) and those that more specifically target FRT (such as those that prohibit the use of such technologies by law enforcement and state governments). Particular attention is given to the different regulatory institutions in the United States, including the federal and state governments and federal regulatory agencies, as well as the different treatment of governmental and private users of FRT. The chapter concludes by considering likely future developments, including potential limits of or challenges to the regulation of FRT.

This chapter reflects on some of the challenges surrounding the broader context of design. Strengthening intersectionality in systems design requires data not just concerning women but also gender minorities, that can be shared and analyzed; it requires ‘finding’ those who should be part of research and creating safe spaces for them. A feminist perspective encourages to prioritize the ‘personal’, recognizing it as a political act of resistance. At the heart of gender equality is the collective dimension of women’s citizenship and their social capital. Alliances in support of gender/social justice in design need to be built using strategies such as participatory infrastructuring and ‘institutioning’ but also acknowledging the importance of feminist trade unionism. The final points raised in this chapter are: how to connect with moves to decolonize discourses and practices of IT design; how to take a feminist perspective with regard to teaching; how to get funded and published; and how to challenge the business models of the software industry that undermine technical flexibility and make gender-sensitive design approaches difficult to implement on a larger scale.

Facial recognition technology (FRT) has achieved remarkable progress in the last decade owing to the improvement of deep convolutional neural networks. The massive deployment of FRT in the United Kingdom has unsurprisingly tested the limits of democracy: where should the line be drawn between acceptable uses of this technology for collective or private purposes and the protection of individual entitlements that are compressed by the employment of FRT? The Bridges v South Wales Police case offered guidance on this issue. After lengthy litigation, the Court of Appeal of England and Wales ruled in favour of the applicant, a civil rights campaigner who claimed that the active FRT deployed by the police at public gatherings infringed his rights. Although the Bridges case offered crucial directives on the balancing between individual rights and the lawful use of FRT for law enforcement purposes under the current UK rules, several ethical and legal questions still remain unsolved. This chapter provides an overview of sociological and regulatory attitudes towards this technology in the United Kingdom; discusses the Bridges saga and its implications, and offers reflections on the future of FRT regulation in the United Kingdom.

Although in all of the EU member states, law enforcement institutions have to adhere to European standards of facial recognition technology (FRT) usage, each country has local national standards that transpose these requirements into the framework of FRT in practice. However, recognising that each society has an important role in controlling the implementation of legal acts, especially where they relate to human rights, society and related interest groups have to regard the proper implementation of FRT regulation as necessary; otherwise it remains declarative and void. If public awareness and pressure to have a law implemented properly are high, the implementing institutions are forced to take action.

This chapter analyses the regulation of FRT usage by Lithuanian law enforcement institutions. Public discussion relating to FRT usage in the media, the involvement of non-governmental organisations, and other types of social control are also discussed. Finally, the chapter considers the changes that may be brought to national regulation of FRT by the EU Artificial Intelligence Act.

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.