MarketSurveillance issues in smart cities
Company Profile

Surveillance issues in smart cities

Smart cities seek to implement information and communication technologies (ICT) to improve the efficiency and sustainability of urban spaces while reducing costs and resource consumption. In the context of surveillance, smart cities monitor citizens through strategically placed sensors around the urban landscape, which collect data regarding many different factors of urban living. From these sensors, data is transmitted, aggregated, and analyzed by governments and other local authorities to extrapolate information about the challenges the city faces in sectors such as crime prevention, traffic management, energy use and waste reduction. This serves to facilitate better urban planning and allows governments to tailor their services to the local population.

Law enforcement
The most discernible use of smart city technology for Government Surveillance arises in law enforcement, where critics consider the accumulation of intelligence through data collection strategies key to intelligence-based policing. The technology available in smart cities includes extensive CCTV installations (such as in London and Dubai), smart traffic sensors in New York and crime prediction software in Santa Cruz, California. Here, the location and speed of every vehicle is recorded and transmitted to the local authority, with a fine issued if the speed of the car exceeds the limit for more than 10 seconds. allowing authorities to warn motorists of incidents ahead or adverse weather conditions. Such technology has a combined preventative and deterrent effect on motorists committing traffic violations. By controlling the speed of vehicles, authorities may minimize one of the more common risk factors in vehicular crashes. Similarly, by monitoring the location of vehicles through a mix of GPS and camera technology, authorities are able to react in real time to minimize heavy traffic incidents and therefore the likelihood of crashes. Predictive policing Predictive techniques in policing are not new, as search warrants are a pre-existing example of authorities acting on the basis of suspicion and prediction in contemporary communities In the context of smart cities, predictive policing is the use of data analytics to determine potential locations of future crime. This allows for targeted crowd management and prediction of dangers related to excessive crowding. Firstly, the level of criminal activity in a particular area sufficient to warrant extra patrols is unclear when predicting the commission of offences. The point at which the probability of crime becomes statistically significant is one which legal scholars and courts alike have had trouble defining. as well as the UK. The latter was confirmed as binding by the European Court of Human Rights on a number of European nations, which includes civil law states. The ability to formulate such “reasonable suspicion” on the basis of big data algorithms is controversial, with some critics arguing that in the absence of active police corroboration of predictive forecasts, there are insufficient grounds to warrant an arrest. with respect to specific individuals. Patterns of crime generated through data analytics are unlikely to generate the level of accurate predictive detail required for police officers to effect an arrest, when compared to informed tip offs. and academic research recommended that such technology be prohibited from use by local authorities, unless they were tied to the investigation of serious criminal offenses. In addition, a major factor in Europe is that predictive policing technology must be exercised in accordance with legislation that is sufficiently clear on the scope of use (foreseeability) and affords persons adequate legal protection from arbitrary uses of predictive data algorithms. Roughly 83% of persons stopped under the program were persons of color. This discrimination was masked through the noise generated by mass data analysis, leading some academics to state that the number of factors within predictive policing algorithms may result in conflicting data and biased sampling. The European Court of Human Rights has also acknowledged the disproportionate targeting of search powers against persons of colour in the UK, highlighting the dangers of smart city technology in predictive policing. == Mass surveillance ==
Mass surveillance
The concept of smart cities is inherently tied to mass surveillance. The benefits derived from smart city technology are dependent on constant data flows captured and aggregated by sensors, cameras and tracking applications. In contrast, critics state that information sharing in smart cities has shifted from horizontal information flows between citizens to a vertical, unilateral process between citizen and government, reflecting concerns about panopticism. while Mobypark allows for the advertisement and renting of available parking spaces. The information collected across these and over 70 other projects in Amsterdam is stored by the City of Amsterdam via a common IP infrastructure. Considering that data from these services is accessible by a primary governmental body, it allows for the possibility of data which is collected from these ‘distinct’ sources to be aggregated. Big data analysis Big data often refers to the use of data analysis and mapping algorithms generate valuable insights from seemingly disparate datasets. The implications of applying such analysis to aggregated data sets are that they allow for a more holistic view of the needs of a particular community to be formed. Within smart cities, this data can be used as a reflexive tool when implemented within the urban ICT framework allowing the Government to better meet the goals of smart cities – improved livability, efficiency and sustainability. that allow for central control, buses in Barcelona now run to a schedule that attempts to minimize the amount of time spent waiting at traffic lights. Big data analysis is not without flaws in its approach This is particularly true when applied to law enforcement, or where data is collected without the willing cooperation and consent of parties involved. Critics argue that there is an element of "mythology" surrounding big data that larger data sets offer deeper insights into urban issues with higher levels of accuracy and objectivity. In the absence of critical insight however, reliance on data alone has little support, as seen in the legal doctrine of reasonable suspicion. Critics of the increasing role played by data-based surveillance for the purposes of law enforcement foresee that such reliance could lead to issues in prosecuting individuals based on a probability-based crime system. Adding to the potential for discrimination, many big data algorithms often create new categories that exceed the scope of regulations designed to prevent against the unfair or discriminatory use of data. While e-governance can improve service delivery and expand the ability to collect data from a single platform, while similar cards have been implemented in Southampton Despite this, such tactics would only minimize and not eliminate their collectable data. particularly when collated from multiple information sources. The storage of data by governments remains opaque, while the potential for cross-sharing data across government services often means that data is accessible by parties with whom the provider did not intend to share the data. where a discussion paper published by the Australian Law Reform Commission confirmed that anonymised data may still be PII. and the European Union (see: Data Protection Directive). In Europe, government technology that interferes with privacy must be based on a "pressing social need" or otherwise "necessary in a democratic society" and be proportional to the legitimate aims espoused. This means that authorities implementing smart cities regimes are at risk of violating privacy laws if appropriate safeguards are not taken. The European Court of Human Rights has held that surveillance mechanisms (including those implemented in smart cities technologies) can violate the right to privacy, especially where domestic legislation does not define the scope or manner of surveillance. Conversely, individuals may find that their data has been used illegally in the implementation of smart cities technology. As much smart city technology is based on open platforms that are often outsourced where a Digital Security metric was incorporated alongside traditional measures of safety such as Personal Security and Health. Panopticism , section and plan of Jeremy Bentham's Panopticon penitentiary, drawn by Willey Reveley, 1791 The English philosopher Jeremy Bentham created a circular prison design, known as the Panopticon, whereby prisoners knew that they were capable of being observed at any time without their knowledge – thus affording the prison officers a position of omnipresence. The French philosopher Michel Foucault re-conceptualized the notion of a panopticon as a metaphor for a ‘disciplinary society’, wherein power relations (and imbalances) can be defined and reinforced. In such a society, power is seen to approach its ideal form by increasing the number of people who can be controlled. One of the major issues with Panopticism in the Smart Cities context is that the 'surveillance gaze' is mediated by the selective biases of the operators of any application or technology, as was shown by a study on the use of CCTV cameras in the UK, where the "usual suspects" tended to be targeted more frequently. Compounding these issues, digitally based panopticism usually views the "visibility" of undesirable characteristics as the problem, and often fails to adequately address matters that are invisible to the surveillance gaze. Police state If a shift toward mass surveillance came to fruition, it could give rise to the development of an electronic police state as a result of the increased surveillance capabilities and law enforcement activities. This represents a distinct narrowing of the purpose of surveillance to that of maintaining social order via improved law enforcement. Van Brakel argues that these changes have already taken place, and that the focus of police has gradually moved towards "front-loading" their intelligence systems with relevant knowledge that can be later sorted and used,. Supporting this institutionalised shift, the House of Lords in the UK argued in 2009 that an advantage of surveillance activities is the ability for the government to provide a more tailored approach to governance, and by extension, law enforcement. == Solutions ==
Solutions
In seeking a middle ground between the societal benefits afforded by big data and the resulting loss of privacy and autonomy, academics have proposed a number of solutions. Komninos refers to the three layers of intelligence in smart cities Many technologies have developed too rapidly to be covered by a single technology-specific regime, while a technology-neutral approach risks being too ambiguous to encourage use or development of the regulated technology. reinforcing the difficulty of establishing a suitable legal regime. One potential legal solution in the UK has been the development of the tort of misuse of private information, which the English Court of Appeal held could potentially be breached by data collection, for which damages may be claimed. Studies conducted by Deakin and Campbell in 2005 identified three types of interaction between citizens and smart cities. They concluded that citizens desire accessible and reliable information and seamless and responsive governments during transactions. Increasing awareness of how, where and why data is collected by the Government establishes the groundwork for a non-adversarial approach to the use of data within smart cities. have seen growth in the use of smart city applications. Furthermore, this process has developed to allow individuals to access their own data in a usable format, In this way autonomy is regained both in relation to awareness of how an individual is affected by the collection of data as well as participation in the actual application of this data to generate information, as new technologies are developed. Accountability In addition to general awareness of the intended purpose of data collection ‘before the fact’, accountability processes ‘after the fact’ are also required. In data-driven processes, particularly in the fields of law enforcement, it is difficult to attribute responsibility to a single body or source, as often the information is derived from a number of different locations. Paskaleva notes that e-governance platforms are particularly suited to democratically generating community support where residents are able to participate in the decision making and implementation process. where citizens are invited to ICT ‘taster’ sessions in local venues, enabling them to learn about the planning, development and design of new smart city technologies. while in Amsterdam, “Smart Citizens Labs” are designed for interaction between citizens and government. These mechanisms have resulted in large levels of enthusiasm for smart city technology, has implemented smart city technology using the Triple Helix Model with positive outcomes. The Triple Helix Model for Smart cities, combining university, industry and government Empirical studies conducted on smart cities in the Netherlands compared the level of ICT penetration to the city’s level of smartness under the Triple Helix Metric, finding a strong positive correlation. A live example of the Triple Helix Model in practice can be seen in the Kista Science City business cluster in Stockholm. Underpinned by the Stokab Model of government provisioned dark fibre, more than 1000 companies including multinational Ericsson, the Royal Institute of Technology (KTH) and Stockholm University reside in Kista, where a database of all children assessed by government services (including police, social services and schools) is maintained by the UK Government, highlights how anonymity is fading due to data-driven technologies. The system allows authorities to predict which children will commit crime in the future and allow them to intervene, based on a number of risk factors and profiling. It is evident that citizens captured by the database as children will no longer be "anonymous" members of society. Given the potential government presumption that parties unwilling to share their information are inherently suspicious, the difficulty of maintaining anonymity in modern smart cities is clearly quite high. == See also ==
tickerdossier.comtickerdossier.substack.com