Police use of facial recognition technologies is on the rise across Europe and beyond. Public authorities state that these powerful algorithmic systems could play a major role in assisting to prevent terrorism, reduce crime, and to safeguard vulnerable persons. There is also an international consensus that these systems pose serious risks to the rule of law and several human rights, including the right to private life, as guaranteed under the European Convention on Human Rights (ECHR). The world’s first case examining the legality of a facial recognition system deployed by police, Bridges v South Wales Police, thus remains an important precedent for policymakers, courts, and scholars worldwide. This chapter focusses on the role and influence of the right to private life, as enshrined in Article 8 ECHR, and the relevant case law of the European Court of Human Rights, in the ‘lawfulness’ assessment of the police use of live facial recognition in Bridges. A framework that the Court of Appeal for England and Wales held was ‘not in accordance with the law’ and therefore in breach of Article 8 ECHR. The analysis also considers the emerging policy discourse prompted by Bridges in the United Kingdom surrounding the need for new legislation, a significant shift away from the current AI governance approach of combining new ethical standards with existing law.

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.

This chapter examines facial recognition technology (FRT) and its potential for bias and discrimination against racial minorities in the criminal justice system. The chapter argues that by defining the technology as an automated process, there is an implied objectivity, suggesting that such technologies are free from errors and prejudices. However, facial recognition is dependent on data used to train an algorithm, and operators make judgements about the wider social system and structures it is deployed within. The algorithms that underpin FRT will continue to advance the status quo with respect to power relations in the criminal justice system, unless both data-based and societal-based issues of inequality and discrimination are remedied. FRT is imbued with biases that can negatively impact outcomes for minority groups. The chapter argues that there is a need to focus on systemic discrimination and inequality (rather than calling for a ban of the technology). While the data-based issues are more straightforward to address, this alone will not be sufficient: addressing broader and more complex social factors must be a key focus in working towards a more equal society.

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.

As facial recognition technology (FRT) becomes more widespread, and its productive processes, supply chains, and circulations more visible and better understood, privacy concepts become more difficult to consistently apply. This chapter argues that privacy and data protection law’s clunkiness in regulating facial recognition is a product of how privacy and data protection conceptualise the nature of online images. Whereas privacy and data protection embed a ‘representational’ understanding of images, the dynamic facial recognition ecosystem of image scraping, dataset production, and searchable image databases used to build FRT suggest that online images are better understood as ‘operational’. Online images do not simply present their referent for easy human consumption, but rather enable and participate in a sequence of automated operations and machine–machine communications that are foundational for the proliferation of biometric techniques. This chapter demonstrates how privacy law’s failure to accommodate this theorisation of images leads to confusion and diversity in the juridical treatment of facial recognition and the declining coherence of legal concepts.

The key ethical requirements for all AI technologies, including facial recognition technology (FRT), is their transparency and explainability. This chapter first identifies the extent to which transparency and explainability is needed in relation to FRT among different stakeholders. Second, after briefly examining which types of information about AI could be potentially protected as trade secrets, it identifies situations where trade secret protection may inhibit transparent and explainable FRT. It then analyses whether the current trade secret law, in particular the ‘public interest’ exception, is capable of addressing the conflict between the proprietary interests of trade secret owners and artificial intelligence transparency needs of certain stake holders. This chapter focusses on FRT in law enforcement, with a greater emphasis on real-time biometric identification technologies that are considered the highest risk.

Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.

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.

Can we trust the judgement of machines that see? Computer vision is being entrusted with ever more critical tasks: from access control by face recognition, to diagnosis of disease from medical scans and hand-eye coordination for surgical and nuclear decommissioning robots, and now to taking control of motor vehicles.

Cities around the world try to set standards for their digital agendas, and constructing smart city ‘roadmaps’. Taking stock of emerging approaches that seek to apply or supplement existing rules on privacy and data protection and to sustain public confidence and support in the face of innovation and change, challenges are explored. Drawing on a growing critical literature, in law, planning, and other fields, that seeks to identify the nature and implications of these developments beyond the promotional language of ‘smart’, the work of Sidewalk’s masterplan for a site in Toronto, and the Los Angeles Department of Transportation’s new and influential approach to mobility data (data associated with ridesharing, ‘micromobility’ such as e-scooters, and in time autonomous vehicles), is assessed. Two facets of urban technology that relate to (and ultimately enable) the delivery of personalised services by public authorities and others are then considered: ratings and reputation (highlighting Chinese cities deploying aspects of the emerging 'social credit’ systems) and facial recognition (noting that the ability to recognise individuals in this way, without a conventional and more deliberate identification (e.g. supplying a name, entering a password, or older biometric systems such as fingerprint scanning), is a key part of many proposed personalised services).

There are various definitions of privacy, and for some time now, privacy harms have been characterized as intractable and ambiguous. In this chapter, I argue that regardless of how one conceptualizes privacy the ubiquitous nature of IoT devices and the data they generate, together with corporate data business models and programs, create significant privacy concerns for all of us. The brisk expansion of the IoT has increased “the volume, velocity, variety and value of data.”1 The IoT has made new types of data that were never before widely available to organizations more easily accessible. IoT devices and connected mobile apps and services observe and collect many types of data about us, including health-related and biometric data.

The IoT allows corporate entities to colonize and obtain access to traditionally private areas and activities while simultaneously reducing our public and private anonymity.

For over a century, the field of forensic science has been applying contemporary technology to the investigation of crime. The imperative to identify offenders, particularly in relation to serious offences, has meant that governments are willing to invest in new technologies to achieve this objective. Fingerprinting, first developed in the late 19th century to identify individuals based on the unique patterns on the fingertips, led the way as one of the earliest means of identifying people, and is still used today in a digitised format.

Privacy law, especially in the form of the right to private life in Article 8 of the European Convention of Human Rights, has addressed police photography, biometrics, and filing systems, including when police identification images are taken in public, outside of the context of arrest. The law slowly came to recognise that the building of institutional identity databases was a meaningful and potentially objectionable practice because it could stigmatise people with information in those filing systems who had not been convicted. This chapter outlines the development of that legal constraint on profiling, as well as its limitations, such as expanding law enforcement intelligence practices. It argues that privacy becomes meaningful for government profiling only when the ‘reasonable expectation of privacy’ test is abandoned, and with a focus on data processing, image identification, and ‘systematisation’.

Chapter 5 describes a uniquely productive type of surveillance known as a binary search. Binary searches reveal no information other than the absence or presence of illegal activity. The Supreme Court has correctly determined that a binary search does not implicate the Fourth Amendment, since an individual does not have a legitimate expectation of privacy in illegal conduct. The cost–benefit analysis theory encourages binary searches, because they are the perfect example of positive sum surveillance: if designed properly, they can increase the level of crime detection without increasing the level of privacy infringement. Soon facial recognition technology and advances in crime recognition software will allow law enforcement to achieve nearly 100 percent enforcement for certain crimes. Such a development, though theoretically desirable, has potentially negative side effects.