3.1 Surveillant applications

The goal of surveillance, a key element of the police métier, is to keep watch over individuals and groups of people. Surveillance can be targeted at specific individuals or to observe masses moving through public spaces. Technological devices have extended powers beyond human visual capacity to include infra-red night vision and can incorporate sensitive listening devices and chemical sensors. Smart surveillance devices are able to recognise faces, extract information from documents and read vehicle number plates. BWV collects information once the officer is on the scene and is currently used mostly as a focused tool rather than a mode of general surveillance.Footnote ³ It can, of course, also be used for general overt surveillance (much like static CCTV) and covert surveillance, though this is prohibited in the UK except in ‘exceptional circumstances’ and where the ‘necessary authorities have been granted’ (Metropolitan Police, 2016). In what Marx (Reference Marx2002) calls ‘new surveillance’, technology is used to extract or create personal or group data, obtaining more information about citizens than would be available from a traditional search or questioning. But BWV here operates to obtain not simply more information, but more enduring information that can be used in later stages of the criminal justice system. The Metropolitan Police states that data from the cameras are automatically uploaded to a secure server and deleted after 30 days unless required as evidence in an investigation or a complaint against an officer.Footnote ⁴ However, if the data are deemed relevant evidence, they could be stored indefinitely (Magee, Reference Magee2016).

As Adams and Mastracci (Reference Adams and Mastracci2017) point out, while BWV purports to surveil police officers, the frame of its view is pointed outwards: to subjects interacting with the officer and victims of crime. The surveillant scope of the BWV camera presents ‘particularised dangers to victims because of the boundaries they are able to cross … [BWVs] bring state surveillance into homes and private spaces, often during what is already the most horrific, vulnerable point in someone's life’ (Adams and Mastracci, Reference Adams and Mastracci2017, p. 324). BWV can also be shared between criminal justice agencies, analysed using face- or voice-recognition technologies and cross-referenced with other information. Given that the faces of half the US adult population are already in searchable federal, state or local databases (Pasternack, Reference Pasternack2017a), body cameras offer unprecedented capacity for intelligence-gathering, which has far-reaching implications.

3.2 Investigative applications

Crime investigation involves the collection and analysis of evidence for the purpose of fact-finding and identifying suspects in specific crimes and in ‘proactive investigation’ of crimes in prospect (Bowling et al., Reference Bowling, Marks, Murphy, Brownsword and Yeung2008, p. 56). BWV can record events at the scene of an incident and interactions between officers and others present; record interviews and statements from suspects and witnesses at the scene; store and analyse information on offences committed; and secure evidence to prosecute or eliminate a suspect from an inquiry. The role of BWV as an investigative tool seems set to grow as algorithms become more adept at sifting through video footage for indexing, searching and triage. The information collected can be categorised by the type of activity or encounter and this can be done spatially, alongside other technologies, to predict crime hotspots (Sanders and Sheptycki, Reference Sanders and Sheptycki2017). Linking BWV footage to face and voice recognition expands the capacity for it to be used to identify suspects. In Seattle, Chicago and Leicestershire in the UK, the police have analysed recorded visual data with facial-recognition software (Adams and Mastracci, Reference Adams and Mastracci2017, p. 321; see also BBC News, 2014; RT News, 2013; Sanburn and Sifferlin, Reference Sanburn and Sifferlin2014). Facial-recognition software has also already been paired and rolled out with dash-cams, allowing real-time recognition (PRNewsWire, 2017).

Taser have trialled facial recognition on BWV data since 2009 and recently acquired Dextro, a company with artificial-intelligence (AI) software capacities that can tag and flag footage and redact sensitive information prior to disclosure (Pasternack, Reference Pasternack2017b). Aguayo et al. (Reference Aguayo2017) devised an algorithm, based on a sample of LAPD videos and additional simulation videos, that uses BWV data to identify foot chases. Their algorithm has achieved a high success rate. Allen et al. (Reference Allen2017) use ‘change-point detection’ algorithms to identify video segments where an officer speaks or interacts with a person, movement into or out of vehicles and buildings, handcuffs someone or is engaged in a foot chase. The purpose of these is to generate automated labelling mechanisms, allowing the police to more easily search through a vast database of recorded videos to find specific interactions. AI can also be used for automated report-writing or to produce written cues for officers writing reports to ‘jog their memory’ (to which we return below). AI can also read BWV data to identify and automatically extract details from ID cards and vehicle licence plates.

The investigative potential of this technology goes far beyond evidence collection, moving into the realm of evidence sorting, with direct probative consequences. However, at present, the scope of the existing technology is limited: it is very difficult to accurately use facial recognition on BWV footage because of the field of view, constant movement (particularly of those in vehicles) and potential lighting issues. It seems clear that the direction is towards higher accuracy and equipment improvement; in 2016, nine of thirty-eight BWV manufacturers included facial-recognition systems or were working towards future inclusion (Hung et al., Reference Hung, Babin and Coberly2016).

3.3 Probative applications

Probative applications are those that have the function of testing evidence or establishing proof of alleged facts. BWV evidence can be collated for the purpose of framing criminal charges and proceedings through which guilt or innocence can be proven. In some instances, video footage can be taken as compelling evidence of wrongdoing or even conclusive proof. BWV represents a technological change that digitises and visually depicts the analysis of evidence from those suspected of crime, or witnesses to it, at the scene. This video can be used as evidence in prosecution and sentencing.

In 2016, the Independent Police Complaints Commission (IPCC) cautioned that undue weight may be attached to the evidential value of BWV footage even though it cannot capture the atmosphere of a situation, events that occurred prior to it being turned on or what might be happening behind the lens. It states:

This is important, because it is unclear whether officers are given adequate training in integrating the technology into their work in a way that does not damage the quality of evidence generated by BWV. The College of Policing guidelines are clear that BWV must operate in tandem with manual note-taking and is not intended to replace it. However, it is highly plausible that note-taking and report-writing to record incidents on patrol or witness statements will eventually be made redundant by BWV footage (Hymas, Reference Hymas2018). A Computing Community Consortium White Paper (Corso et al., Reference Corso2015) recommended that automated video summarisation using indexing algorithms should be created for all videos to improve officers’ memory of events. This would certainly supersede note-taking rather than being supplementary or complementary to it. Automated report-writing based on footage is now technically possible and would mark a shift in policework similar to the introduction of tape recording in police interviews and the introduction of evidence thirty years ago (see Willis, Reference Willis1984). Efficiency is a key justification for the introduction of BWV technology. A UK Home Office study reported a 22 percent reduction in the time devoted to paperwork and case preparation due to evidence collected from BWV (Goodall, Reference Goodall2007).

Video recordings are now used extensively as evidence in the legal system, particularly due to the proliferation of recording devices in mobile phones, body-worn devices, CCTV, satellites, drones and dashboard cameras. However, the meaning of this visual evidence is rarely derivable on its own and it is often interpreted in conjunction with testimony and other reports. Gates (Reference Gates and Maxwell2016) notes that BWV could also be a reaction to what the police perceive as the ‘uncontrolled visibility’ created by the prevalence of smartphone cameras and social media, allowing them to take back control. This is sometimes termed ‘camera view bias’, where looking at a video filmed from a certain perspective can cause the viewer to adopt an interpretation favouring that vision (Williams et al., Reference Williams2016). There is, of course, the potential to record from a different vantage point: that of the watching citizen. Officers have often been reluctant to be photographed on the job, fearing that their actions may be misinterpreted, and have sometimes been reported to engage in ‘camera-friendly policing’ when being watched, altering their presentation and body language to make footage more favourable (see Sandhu, Reference Sandhu2016). It is unclear whether citizens who are filmed by the police alter their behaviour in similar ways.

There is some evidence that BWV can improve prosecution outcomes in domestic-violence cases by providing evidence from the first police response (Palmer, Reference Palmer2016, p. 141). A randomised control trial in Essex found that BWV increased the proportion of domestic-abuse incidents that resulted in a criminal charge but had no impact on guilty pleas or conviction rates (Owens et al., Reference Owens, Mann and McKenna2014). A study in Arizona concluded that BWV in domestic-violence cases was more likely to result in a guilty plea or verdict (Katz et al., Reference Katz2014). However, the presence of evidence does not always make a difference; where victims retracted their statements or did not support prosecution, the evidence did not prevent cases from ending in no further action. Palmer (Reference Palmer2016) found that the benefits of enhancing prosecutorial outcomes is mixed: public-order offences have a generally high degree of prosecutorial success because there is a filtering of offences before they reach trial. BWV is unlikely to have a significant impact on family violence, sexual assault and other assaults, however, because the police are generally not there to record the act itself (Palmer, Reference Palmer2016, p. 142). The claim is that recordings as evidence will improve convictions because the victim will not have to give testimony in court.

In late 2017, the Home Office tabled plans for using BWV in interviews with suspects away from the police station (Home Office, 2017b). The justification for this was to increase the time spent on the front line, minimising the time spent in trips to and from the police station. This marked a shift in both the location of interviews and the modes by which evidence was collected. Adams and Mastracci (Reference Adams and Mastracci2017) warn that recording statements made by victims of sexual offences can have a detrimental effect when front-line officers are not trained in appropriate victim-interviewing techniques. An interview conducted in a chaotic and difficult setting can memorialise on video evidence that could harm the complainant's case and cause further victimisation in court (Adams and Mastracci, Reference Adams and Mastracci2017).

There are also concerns that footage may be used to coerce plea bargains, even where the evidence is not definitive (Mateescu et al., Reference Mateescu, Rosenblat and Boyd2016). None of these concerns is unique to BWV, but this technology collects information on all surrounding activity in the field of the police officer, capturing everyday interactions and any sound within its range. This makes the field of interpretation much larger than a photograph or even a static surveillance camera. A 2014 review found that early guilty pleas were obtained in 91 percent of cases where camera footage formed part of the evidence and the BBC said this allowed ‘697 officers to be on the streets rather than in the courts’ (Ellison and Adams, Reference Ellison and Adams2017). Grampian Police guidelines note that ‘BWV evidence has negated the need for a high number of Officers to attend Court as Police witnesses and has also reduced the demand on Officers to provide statements for court purposes’ (Police Scotland, 2015, p. 4). This framing is telling, particularly in respect of how BWV automates probative functions of the police: it prioritises a vision of policework where the police are on the streets ‘tackling crime’ and disparages work done in the courts to ensure a fair trial as inefficient use of resources. In other words, the work of the police off the streets is a secondary function, to be done away with and automated if possible. This points to a significant change in the nature of everyday policework. Technology is seen as an obvious and effective solution to the collection and analysis of evidence, even though its real-world effects and impact on behaviour are unknown.

Hartzog et al. (Reference Hartzog2015, p. 1788) warn that ‘automation bias’ – the ‘human tendency to irrationally trust automated decisions’– is likely to grow, with serious consequences for how BWV footage is interpreted in the courtroom. As a result, it becomes all the more important to understand the extent to which and how BWV footage is used in criminal trials (such as how often it results in convictions and guilty pleas) and on the types of charge in which guilty pleas or convictions are most likely to occur. While there are protocols on how camera footage is to be uploaded and stored, no records seem to be kept of how BWV footage is used in trials (Big Brother Watch, 2017). If this is the case, there is no empirical way to test claims that technology will improve either the administration of justice or sentencing.

3.4 Regulatory applications

Regulatory applications are those that seek to make police conduct transparent, to regulate and hold the police accountable for their actions. Civil-society and government organisations assert that BWV will hold the police better to account in cases of police misconduct or fatal shootings by providing a full account of what transpired in each incident and building community confidence in the police. BWV, in this view, has the potential to change the behaviour of the police and the people that they interact with, and affect criminal justice outcomes. The American Civil Liberties Union (ACLU), for example, say that, although they take a ‘dim view’ of the proliferation of surveillance cameras, BWV is different because of its:

In the aftermath of the shooting of Michael Brown in Ferguson, Missouri, more than 140,000 people signed a White House petition in favour of creating a ‘Mike Brown Law’ that would require the police to wear body cameras. The Obama administration had offered partial federal funding for police departments that wanted to buy the technology (United States Department of Justice, 2015) and Taser, a leading manufacturer of the cameras, have called them a ‘community transparency tool’ (Li, Reference Li2014). The Metropolitan Police themselves rolled out head-mounted cameras in 2017, claiming that this was part of an effort to boost transparency (The Telegraph, 2017).

A key argument made by civil-society organisations and activists in favour of BWV is its potential to regulate police conduct, collect evidence on misconduct, deal with internal complaints and improve public image. Corso et al. (Reference Corso2015) argue that BWV footage could function as an early warning system, capturing data on officers who repeatedly use force or do so prematurely rather than relying on officers’ self-reports of use of force. Body-worn cameras that self-activate when officers are running, raise their weapons or open car doors have been proposed; but these also assume that officer misconduct only happens in these narrowly defined scenarios (Mateescu et al., Reference Mateescu, Rosenblat and Boyd2016, p. 125). Other potential regulatory uses of the footage are in training materials for new officers, to give them a sense of street situations (Mateescu et al., Reference Mateescu, Rosenblat and Boyd2016, p. 125). Additionally, footage can be given out to private individuals or companies in order to train machine-learning algorithms and it is unclear what regulations apply to distributing BWV in this way.

Another push for the technology comes from the belief within police departments that the use of BWV will result in fewer complaints against officers, and therefore less money will paid out in civil lawsuits.Footnote ⁶ The influential Rialto randomised control trial (Ariel et al., Reference Ariel2016) found that BWV brought about a 93 percent drop in the numbers of complaints filed against officers when they used BWVs and a 50 percent reduction in the use of force (Ariel et al., Reference Ariel, Farrar and Sutherland2014; Reference Ariel2016; Ariel, Reference Ariel2016). However, a similar study by the same researcher also found that, where officers could choose when to turn cameras on and off, the use-of-force rates were 71 percent higher (Ariel, Reference Ariel2016). In one qualitative study, it was found to reduce the scope for unlawful stop-and-searches: ‘As an officer searches one male associated with organised crime, the man points at the camera: “This [BWC] has ruined it for you, hasn't it?”’ (Rowe et al., Reference Rowe, Pearson and Turner2017, p. 87). However, a Home Office-funded trial found that the devices had no impact on the number or type of stop-and-searches conducted, on the proportion of arrests for violent crimes and ‘no evidence that cameras changed the ways officers dealt with either victims or suspects’ (Grossmith et al., Reference Grossmith2015). This study did, however, find a reduced number of allegations against officers, particularly in relation to interactions with the public (Grossmith et al., Reference Grossmith2015).

The evidence of behavioural change is, so far, mixed. In a study conducted on a larger police force, complaints (generally of misconduct) against officers increased and use of force rose by 17 percent (see Ariel, Reference Ariel2016). The evidence on the use of BWV varies to a great extent on the sample size and methodology used, which often makes it difficult to compare the outcomes of one study against another. For example, the Rialto trial monitored the response of the police in all activities, while the study in Denver only looked at call-to-response cases (not including instances of police-initiated contact). These discrepancies, along with those of place and circumstance, make it very difficult to arrive at a conclusive understanding of how the use of BWV affects police behaviour.

Officer perceptions of BWV are also mixed: in some trials, officers have expressed discomfort with what they see as intrusive surveillance measures that erode their discretion, introduce enhanced liability for reflexive decisions made on the job and are indicative of a mistrust of officers by the public and by management (see Ariel, Reference Ariel2016, pp. 752–753). However, other studies have shown that police perceptions of BWV changed over time as officers reported growing familiarity with the technology and an appreciation for its benefits (Katz et al., Reference Katz2014). A survey of law-enforcement command staff found that staff with less experience tended to be more sceptical than more experienced officers of the potential for BWV to make officers and communities safer, though both younger and older officers appreciated its benefits (Smykla et al., Reference Smykla2015). Other surveys found that a majority of officers are comfortable with the technology.

4.1 Changing, not eliminating, the craft skills of policing

The introduction of BWV in everyday policing points to broader changes in the world of work and in the production of information. Workplaces are being transformed by the introduction of new forms of technology, whether that is used to monitor workers and their efficiency or to monitor the work process itself. When considering the growth of automated policing, where policing skills are converted so as to operate automatically and are replaced by the introduction of technology, BWV exemplifies a case where the process is informated by the generation, use and interpretation of footage.

As Zuboff notes,

Gates (Reference Gates and Maxwell2016) argues that BWV heralds the growth of a ‘police media economy’, where the practice of policing now encompasses cultural media work that invests recordings and images with meaning. BWV functions as a way for officers to ‘create representations of their encounters on the job, self-representations of their subjective experiences’ (p. 3). It also creates forms of work ‘to process, archive, search, circulate, and render authoritative interpretations of video generated by body-worn cameras’ (Gates, Reference Gates and Maxwell2016, p. 3). Joh (Reference Joh2018a) argues that deskilling is a major outcome of automating policework. However, once the labour that is undertaken in categorising, uploading, sorting and analysing video evidence is considered, BWV perhaps indicates a qualitative shift towards re-skilling rather than eliminating labour by creating new kinds of scalable, infrastructural labour (Gates, Reference Gates and Maxwell2016, p. 3). The roll-out of BWV has required a great deal of labour to navigate technical and implementation problems (see Ellison and Adams, Reference Ellison and Adams2017); while BWV can cut some costs, it requires continual work and investment to remain operational.

James Rule's (Reference Rule1973) study of the introduction of radio into police cars is instructive as an earlier example of how the introduction of technology transformed the communicative capacities of the police. For example, it increased officers’ available patrol time by reducing the need to stop at phone-booths to make background-check calls. However, radio did not eliminate the time and energy involved in police communications. As Magnet and Gates note, Rule's account provides an important reference point for ‘understanding transformations in police surveillance practices, especially the translation of human labour into automated, technical systems’ (Reference Magnet and Gates2013, p. 5). Police officers have witnessed many changes to evidence collection in recent decades, each of which led to a corresponding change in officer practice. The police have moved from burning DVDs by the thousand to storing video on laptops and external drives. This has become an overtime issue in some instances because officers ‘worked beyond their standard working hours to try to manage video’ (Gates, Reference Gates and Maxwell2016, p. 16). If we think about the applications for which a technology is used and consider more carefully how human labour interacts or intervenes in processes, it is clear that automation is happening as part of a broader complex of human interaction, interpretation and decision-making. In the case of BWV, machines are replacing police manual labour with more powerful sensory capacities than human beings. The machines never tire and have infinitely greater memory, search, data-processing, data-linking and analytical capacities. In other instances, to paraphrase Arendt, machines are guiding the hands of the police, transforming the nature of policework such that human beings and machines work together seamlessly.

Zuboff's (Reference Zuboff1988) analysis, based on close observation of the introduction of technology in factories and offices in the 1980s, makes an important observation about the ways in which people resist the controlling functions of technology. Even where mechanisms are designed to protect workers – such as automatic closing doors installed in a factory to exclude toxic gases – people find ways of disabling, disrupting and co-opting mechanical devices. The aversion to automation in the workplace seems particularly acute in policework; Khan (Reference Khan2015) describes it as ‘rampant technophobia’ at all levels of policing. Its features are resistance to automation, criticism of and opposition to technological changes, unwillingness to learn to use devices and resistance to training. Historically, this has manifested itself in the sabotage of equipment: for example, in Chicago, it was found that 80 percent of dashboard-mounted videos did not record audio due to ‘officer error’ and ‘intentional destruction’ (Balko, Reference Balko2016). Khan explains that police technophobia is caused by an action-oriented police culture; officers feel their discretion is being eroded and that technology places the police ‘under a microscope’ that will test their performance and hold them to account. There is also concern about civilianisation and the creation of new hierarchies. Despite a generally technophobic culture, however, it seems clear that BWV technology is advancing rapidly through police organisations; it already seems to be widely accepted by front-line police officers.

4.2 Manual vs. automated policing: automation, discretion and decision-making

It may be useful to contrast manual and automated policing. Manual policework involves the use of the physical body (hands, ears, eyes and brain) to observe, record and make sense of the world. Police generally have wide discretion in the use of these physical capacities and craft skills. Human volition lies at the heart of the process and officers are allowed to make their own judgment in procedures and decision-making. Manual processes have the virtue of human judgment and adaptability, and yield appropriate emotional responses such as attentiveness, sympathy and kindness. The downside is that manual policing is susceptible to error; unpredictable and inconsistent actions; inappropriate emotions such as anger, frustration, disgust and contempt; and provides scope for prejudiced and discriminatory decision-making.

In automated policing, electronic and mechanical devices replace the human labour of surveillance, investigation and case preparation. Automation reduces discretion. For example, once video recording has started, the officer wearing BWV loses some control over what events to film, how much to record, and the analysis and reporting of the recorded information. Automation offers the advantages of timeliness, predictability and consistency; it reduces the risk that controls will be circumvented. As with all technologies, there are potential risks, including privacy intrusion, reliance on inaccurate systems and unauthorised access to, changes to and potential loss of data.

Technology is not the only way to reduce police discretion, of course. Tighter rules, closer supervision, targets, quotas, more stringent record-keeping, external surveillance and oversight can all reduce the opportunities for subjective judgments and decision-making. The absence of a machine does not necessarily mean wide discretion, though automation generally does have the effect of reducing discretion by rendering actions visible. On the other hand, automation can be disrupted and returned to manual control by allowing officers discretion about when to use their mental capacity and physical dexterity (manual in a literal sense, as in ‘with the hand’) to switch the camera on and off. Preventing manual override and the potential for selectivity and susceptibility to human error would require BWV either to be switched on permanently or the on/off function to be fully automated.

BWV connects police surveillant, investigative, analytical, probative and regulatory functions in new ways. However, any decision to activate BWV cameras is currently entirely at officers’ discretion. The question of officer discretion is central to how we conceive of automation – if officers can decide when to turn the camera on, is this still within the realm of context-specific decision-making? Or does it represent part of the ‘automating’ impetus towards consistent law enforcement? Arguments about whether officers should continuously record or have the discretion to decide when to begin filming are at the heart of this issue.

At present, officers in the UK exercise discretion as to when the camera should be turned on. In the UK, citizens have no right not to be filmed. The College of Policing guidelines for the use of BWV state:

This clearly puts the onus on the officer to make a context-specific decision. The guidelines state that BWV can provide useful evidence on incidents occurring during night-time patrols as well as domestic-abuse cases, but its use in stop-and-search encounters expressly cannot be for the purpose of identifying the subject of the stop. Officers are encouraged to make a verbal announcement that the equipment has been activated when they begin to record and should indicate why they are discontinuing recording before concluding.

There is more speculation on the point of discretion in the US, where some operational guidelines specify which law-enforcement encounters must be recorded. These include (1) calls for service and en route to emergency calls, (2) pedestrian stops and stops on grounds of reasonable suspicion, (3) traffic stops, (4) foot and vehicle pursuit, (5) consensual and warrantless searches, (6) execution of search warrants, (7) arrests and detentions, (8) transports and (9) other adversarial encounters (Brennan Centre for Justice, 2016). This encompasses a wide range of police activity and is distinct from the current UK ‘incident-specific’ approach, which gives greater scope to officer judgment. The ACLU position is that, from an accountability perspective, continuous recording allows the best outcomes, but that this raises privacy issues for the public and for officers, turning BWVs into generalised surveillance tools. The ACLU recommendations also include an evidentiary presumption against an officer who is wearing a body-worn camera but who failed to record an encounter that later gave rise to a complaint. These recommendations are important to consider because, at the moment, there is no incentive to use cameras consistently nor is there a clear understanding of their evidentiary outcomes. The question of discretion is also complicated in other ways: some cameras do not have the battery life to permit continuous recording, and the use of cameras also multiplies the labour and infrastructure costs involved in storing and processing recorded video data.

The ability for officers to choose when to turn recording on and off constrains transparency and accountability. For Taylor (Reference Taylor2016, p. 131), ‘[c]ameras could be considered the equivalent of the police notebook, but only if it is accepted that the pages of the notebook can be rewritten, edited, modified; even torn out entirely’. Taylor argues that ‘limiting the discretion of police officers to select when to record is critical [to ensure] greater transparency, fairness and accountability’ (Taylor, Reference Taylor2016, p. 131). BWVs represent an intrusion upon a citizen's privacy and can have disproportionate outcomes in a way that CCTV cameras do not – BWVs can listen in on conversations and are attached to the officers who seek out individuals (Miller et al., Reference Miller and Toliver2014, p. 11). There is a tension here: on the one hand, BWV is supposed to make the police more accountable by consistently collecting evidence of their interactions with subjects. On the other hand, officer discretion to not record helps to meet subjects’ reasonable expectation of privacy in the same way as the discretion not to arrest protects liberty.

4.3 Discretion, decision-making and discrimination

Questions of the impact of discretion on police decision-making are particularly important where children and minority groups are concerned. A crucial issue concerns the ways in which innovation in the collection and analysis of police data leads to practices that amplify inequalities of age and ethnicity. This is what Ferguson calls the ‘black data’ problem (Reference Ferguson2017, p. 131), which comprises overlapping concerns with data-driven policing, transparency and racial inequality. Racial inequalities in the US (as well as in the UK and elsewhere) have persisted over a long period of time. People from Black and ethnic minority communities are more likely to come into contact with the police (see also Bowling and Phillips, Reference Bowling and Phillips2002; Reference Bowling and Phillips2007) and therefore, like other crime data, video images of people of colour are more likely to be captured, stored and potentially used as evidence.

Ferguson argues that data-driven policing ‘reifies many of the systemic inequalities of traditional policing’ (Reference Ferguson2017, p. 132). Decisions about the targeting of policing in terms of place is based on data that encode ‘prior decisions of a criminal justice system that disproportionately punishes people of color’ (Ferguson, Reference Ferguson2017, p. 132). Suspect-based targeting also reflects prior arrests, suspected gang associations and so on, which in turn reflects the ‘socioeconomic impacts of traditional policing patterns’ (Ferguson, Reference Ferguson2017, p. 132). These forms of policing carry a large margin of error, can legitimise profiling based on race, gender and socio-economic factors, and may produce many false positives (Ferguson, Reference Ferguson2017, p. 132). Big Brother Watch, for example, noted that Durham Constabulary used BWVs to gather updated images of ‘known villains’ during stop-and-searches (Reference Ferguson2017); and the College of Policing guidelines state that there is no prohibition on taking photographs during stop-and-searches under the Police and Criminal Evidence Act (1984), and therefore the decision as to whether it is proportionate to use BWV in stop-and-searches is at the officer's discretion (Home Office, 2017a).

In R. v. Commissioner of Police for the Metropolis (2009),Footnote ⁷ it was held that photographs or other recordings by the police do not prima facie contravene police powers, but must be justifiable on a case-by-case basis. This case involved photographing a peaceful protestor in order to identify potential offenders and the photographs were retained for an unreasonable length of time against the request of the protestor. The Court of Appeal held that the taking and retention of photographs pursued legitimate aims (the prevention of disorder or crime and the safeguarding of public safety) but, once it became clear that the individual had not committed an offence, the images had to be deleted. What is unlawful here is not recording an image, but retaining it beyond the point at which there was reason to suppose that the individual had engaged in criminal conduct. Arguably, while this does protect citizens from unjustified long-term surveillance, it also means that a much higher level of discretion is left to the officer in determining which cases are worth recording. It is also unclear how an individual citizen would be able to prove per College of Policing guidelines that the very use of BWV was unjustified. The central tension between privacy and discrimination outcomes and the risks of discretion remain unresolved. These have wider impacts if the footage is linked to other databases with wider information being made immediately available to the officer.

Since it is more likely that people from Black and minority ethnic communities in the UK will be caught on camera, as they are more likely to come into contact with the police (Bowling and Phillips, Reference Bowling and Phillips2002), there is a greater risk of this footage being mined by various recognition technologies. This is akin to the overrepresentation of Black people in the DNA database; the disproportional representation of minorities in one police database that is then linked to others could have disproportionate effects across the spectrum of evidence collected on citizens by the police. This is because BWV is part of a larger assemblage of surveillance tools, such as CCTV and dashboard cameras, which already capture a disproportionate number of images of individuals who spend a great deal of time on the streets without access to private space (homeless people, young people and people of colour).

BWVs could also be used to compile ‘risk information’ on individuals and localities, overrepresenting minority neighbourhoods on police databases. Because predictive policing is based on prior arrests and information about suspected gang associations, it is very likely to reproduce and reify the systemic inequalities of traditional policing (Ferguson, Reference Ferguson2017, pp. 131–142). Although it is claimed that big data technologies will avoid racially biased effects, this cannot be achieved without confronting the complexities of ‘new technologies and old practices’ (Ferguson, Reference Ferguson2017, p. 142). Discretion here can meld with a policing practice that has traditionally been used to target minority communities, potentially generating discriminatory outcomes. Returning to Ariel et al. (Reference Ariel2016), where discretion in turning the camera on and off was permitted, use-of-force rates were 71 percent higher than where discretion was not permitted. They concluded that BWVs can ‘reduce police use of force when … officers’ discretion to turn cameras on or off is minimized’ (Ariel et al., Reference Ariel2016, p. 454). This is worrying, and runs counter to the stated purpose of introducing BWV technology in the first place.

Reducing discretion can cut both ways and could well have discriminatory outcomes of its own. Mateescu et al. (Reference Mateescu, Rosenblat and Boyd2016, p. 123) warn that the use of BWV may also change policing more dramatically, expanding low-level arrests to meet quotas, as officers may be more likely to follow procedure when recorded on the job. As Guidou (Reference Guidou2012, quoted in Eyssartier and Hamelin, Reference Eyssartier and Hamelin2013) notes, ‘the job of police officers is changing rapidly with a redefinition of work, a redistribution of tasks, and a developing technology, which offers some advantages but hinders the freedom and the autonomy of the agents’. In this case, BWV is seen as having a potentially detrimental effect by reducing the scope for discretion through heightened visibility on the job. This points to the risk of automation embedding or reinforcing ‘machine bias’ produced by pre-existing discriminatory practices (see Angwin et al., Reference Angwin2016). As Ferguson (Reference Ferguson2017, p. 48) demonstrates, ‘disproportionate minority contact with the criminal justice system seeds the algorithms that generate heat-list-inspired predictive models’. Adams and Mastracci (Reference Adams and Mastracci2017) argue that officers should have a wide amount of discretion in deciding what BWVs record because the technology is potentially very intrusive and can represent the state surveillance of people's private lives and homes. On the other hand, ‘a camera that can be switched off, or wilfully turned away from a police interaction with an assailant without consequence, cannot increase accountability or reduce poor policing practice’ (Taylor, Reference Taylor2016, p. 130).

Either way, given the amount of discretion that is left to officers in the ‘incident-driven’ approach, BWVs automate the process of surveillance, evidence collection and analysis only when the camera is switched on manually. Where discretion predominates, humans are able to make context-specific decisions that they believe best suit the circumstances. This may be seen as inefficient. However, Hartzog et al. (Reference Hartzog2015) suggest that ‘inefficiency is an effective safeguard against perfectly enforcing laws that were created with implicit assumptions of leniency and discretion’ (p. 1763). In other words, perfect and consistent law enforcement can be the enemy of fairness in criminal justice. These arguments have also been made in the context of road policing, with some officers complaining that automation removes the opportunity to explain to people the dangers of speeding, the gravity of their offence or give them a chance to explain their behaviour (Eyssartier and Hamelin, Reference Eyssartier and Hamelin2013).

The ‘techno-fix’ in the field of road policing not only serves to monitor and punish ‘risky populations’, but is also commonly experienced as unfair and unjust by ‘respectable, upstanding members of the community’ who break road-traffic laws (Wells, Reference Wells2008, p. 802). The virtues of automated speed detection, such as consistency, neutrality and impartiality, are not perceived as just when those caught do not accept the legitimacy of the speed restrictions, the placement of detection devices and the denial of ‘voice’ in proceedings. Wells (Reference Wells2008) argues that such personal factors as ‘common sense’, discretion and respect are vital to achieving a just experience. These are conditions of human enforcement and interpersonal encounters. They do not apply to human–machine interactions, which can be experienced as ‘too impartial, neutral and consistent’ (Wells, Reference Wells2008, p. 814, emphasis in original). Many of the drivers interviewed by Wells demanded that ‘all kinds of biases and discrimination be put back into the system’ (Wells, Reference Wells2008, p. 814, emphasis in original). This may not be a justifiable demand because, as Wells (Reference Wells2008, p. 808) points out, unbiased enforcement can be experienced as too fair ‘in that it does not discriminate enough for people who are used to discrimination working in their favour’. The fact that ‘low visibility’ may allow discrimination to persist in policing work has formed part of the push to introduce the technology. Nonetheless, the widespread perception that automatic processes are unfair undercuts one of the stated objectives of BWVs: improving community trust and police accountability.

To the degree that discretion plays a determining role in the use of the technology, BWVs do not entirely replace human behaviour. To the extent that we assume that automating a process involves consistent outcomes and perfectly enforced laws, the BWV camera is a machine embedded in human behaviour and discretion. It forms part of a larger surveillance and investigative apparatus alongside CCTV cameras, automatic licence-plate readers, dashboard cameras, facial recognition, gait recognition, etc. The interpretation of the footage is still heavily dependent on humans. It is, however, a machine that changes human behaviour and automates some, but not all, processes of surveillance, investigation and evidence testing. The risks of prioritising either consistent decisions through automation or context-specific decision-making through discretion are magnified by the potential for ‘mission creep’,Footnote ⁸ with ramifications throughout the criminal justice process for those caught on BWV footage.