Smart cities and the imagined objectivity of data

Whether it is about asking your smart home device to close the shutters, selecting the right employee for a vacancy, detecting criminals, or reducing pollution: smart technologies are finding their way into the mainstream society.

One of the reasons for the growing popularity of smart technologies could be that they are driven by artificial intelligence (AI) and are therefore capable of making sense of big data sets (Kitchen, 2014). Those data sets can be derived from different systems such as surveillance cameras, heating or noise sensors, and applications such as Google Maps which makes visible how many people are at a certain place at a specific time. Another reason for the popularity of smart technologies might be that they are assumed to reduce, or maybe even end, the arbitrariness and prejudice in knowledge construction and decision-making, in contrast to their human counterparts (Kitchen, 2014).   

The last twenty years there is a strong tendency to move from rural areas of the world towards more urban places (Yu & Xu, 2018). According to Damori (2014) more than 52% of the world population in 2014 lives in cities . As a mixture of the trend of urbanization and smart technologies, there comes more attention for Smart Cities as well (Yu & Xu, 2018). Kitchen (2014) elaborates on the concept of Smart Cities and describes that a Smart City is full of digital and technological devices that are built into its environment. He states that those built-in digital devises are used to: 

“monitor, manage and regulate city flows and processes, often in real-time, and mobile computing (e.g., smart phones) used by many urban citizens to engage with and navigate the city which themselves produce data about their users (such as location and activity).” (Kitchen, 2014, p. 2)

The citizens of Smart City’s therefore are being monitored on specific aspects of daily life. Without them sharing information actively, this information is generated automatically and the agency therefore shifts from the individual to the one monitoring. This collection of data, eventually, is used to gain a more in-dept understanding of the particular city and to design value free and evidence-based urban policies (Kitchen, 2014). This, however, might have consequences for the citizens of the Smart City. This article seeks to examine the following question: What is the influence of this data aggregation and the data-based urban policy on the citizens of a Smart City?

In order to answer this question, the first section of this article focusses on several examples of smart cities and the institutions gathering the data. Thereafter, the consequences of the embedded technologies and new forms of surveillance will be discussed. The last section of the article will question the claimed objectiveness of data processing.

Same same but different 

All over the world countries are piloting the adoption of Smart Cities, in total there have been around thousand Smart City pilots. China, undoubtedly, takes the cake with a total of 269 ongoing pilots (Yu & Xu, 2018). In Hangzhou, for instance, there is a project called City Brain. City brain is created by multinational Alibaba and has different projects. One of them is Community and Public Safety, there they use video analysis technology and video recognition algorithms to take preventive measures to ensure the safety and security of the public (GOV Insider, 2018).

Another project of Alibaba’s City Brain is Traffic Congestion and Signal Control in which surveillance cameras and sensors are used at different places in the city to collect information on road conditions in real time (GOV Insider, 2018). Subsequently, this real time collected data is send to an AI Hub, in which traffic signals throughout the whole city are managed. Similar projects can be found in other Chinese cities such as Shanghai with their Citizen Cloud, the Huawei Pay App from Beijing and the Smart Medical App from Guangzhou. 

As can be seen from the examples above, every city has its own focus and the data collection can be carried out by both public and private institutions (Damori, 2014). This raises questions about ethical issues because who can be held responsible for the big data set if it is all different sensors who enable data aggregation? The fact that every smart phenomena is one of its own is also a complicated factor of Smart Cities, because this might mean that it is not evident for the citizens where they are being observed and where the collected data is used for. The consequences of these forms of data collection and monitoring on the citizens of the Smart Cities will be discussed in the next section. 

Track and Trace

As might have become evident already, the actions and interactions of citizens in Smart Cities are being tracked across a number of domains (Kitchen, 2014). Being in a public space, such as driving a car on the highway or walking through the city in Hangzhou, exposes the individual to surveillance technologies. The citizens, however, do not know exactly when someone is watching them because the technologies are embedded in the urban environment. Therefore, the citizens are being watched while the governments and/or private institutions are the one looking. As a consequence, citizens are more likely to act in a socially accepted way in order not to be deviant or stand out (Brighenti, 2007).

In that sense, the Smart City can be compared to a panopticon (Kitchen, 2014). A panopticon is a building designed to allow all people in that building to be observed by only one person, this person is not visible for the other people in the building. Therefore, the people in the panopticon will behave in a way as though they were watched because they never know when someone is observing them. Therefore, this new form of data aggregation might also have the implicit function to direct into normative behavior. 

This connects seamlessly with the concept of orders of visibility from Hanell and Salö (2017). Hanell and Salö(2017) state that social structures in society determine how knowledge is stratified and which knowledge will be visible and which knowledge will not. In that sense, knowledge and power cannot be seen separately from each other because the person with power determines which knowledge should be considered to be more legitimate, and knowledge which is more legitimate is perceived to have more power (Foucault in Hanell & Salö, 2017). In both examples, Smart City and panopticon, the person watching is the person with the power because they are also the one deciding which knowledge and behaviour is made (in)visible and which behavior is considered to be the norm. It should be taken into account, however, that all knowledge is socially constructed and therefore dependent on many factors (Berger & Luckmann, 1966). 

This socially constructed nature of knowledge is often forgotten or excluded when talking about Smart Technologies. Take for instance Hangzhou’s City Brain project ‘Community and Public Safety’, in which camera surveillance and video recognition algorithms are used to determine where an unsafe situation is taking place in order to allow authorities to take preventive actions. In this Smart City project, it is not explained anywhere clear what is considered as an unsafe situation.

Although the technologies are smart, the norms cannot be decided by the data or algorithm itself, these decisions are made by humans first. Taken into account the socially constructed nature of knowledge, this means that data and/or algorithms are not that objective as they pretend to be. The next section will elaborate on the concept of data objectivity in more extent. 

Don’t blame us, it is the data!

However, people making use of smart technologies and algorithmic based data, often use the claim that their data is objective because there is no human inference in the data gathering (Gillespie, 2014). A camera or a sensor itself does indeed has no political agenda or prejudices. This makes that projects like Smart Cities look like they are based on objective knowledge. However, an algorithm cannot function without human inference because there always need to be ideas, people and context to store them (Kitchen, 2014). 

First, it needs to be decided which data should be gathered. The surveillance cameras used at the City Brain to track the traffic in Hangzhou are not asked to gather only data about the color of the clothes from the people in the car, they are asked to gather data about for instance the speed of the cars and so on. Those decisions can only be made by humans in first place. Additionally, the data needs to be made algorithm ready in order for the algorithm to make sense of it. Moreover, the real time gathered data from the surveillance cameras or other embedded smart technologies is used alongside data gathered over time (Kitchen, 2014).

The data control centers, such as the AI-Hub in Hangzhou, integrate and bind together different sets of data to one big data set which is eventually used to make decisions upon (Kitchen, 2014). The decisions will therefore always depend on the presuppositions about what matters for that specific goal. As Gillespie describes: “from this perspective, we might see algorithms not just as codes with consequences, but as the latest, socially constructed and institutionally managed mechanism for assuring public acumen: a new knowledge logic.” (2014, p. 192). 

this claim of objectivity is often used by its developers and policy makers in order to guarantee that the societal decisions are based on what appear to be common-sense and evidence-based findings.

The example of the color of the clothes form the people in the car that I just gave, might not sound to disastrous. However, those decisions are also made with regards to more sensitive issues, such as Community and Public Safety in which needs to be decided what is considered to be an unsafe situations. In that specific case it might be possible that specific groups in society are expected to be more threatening to the public safety. This is then again based on the socially constructed knowledge and subsequently the video recognition algorithms are learned to look out for those certain groups even more. This, eventually, could lead to disadvantages for specific groups in society because they are made visible in a specific way that is legitimized by the powerful ones (Hanell & Salö, 2017). 

In summary, algorithmic gathered data is never fully objective. However, this claim of objectivity is often used by its developers and policy makers in order to guarantee that the societal decisions are based on common-sense and evidence-based findings. 

The data-based urban policies of a Smart City

The purpose of the current study was to determine the influence of data aggregation and data-based urban policies on the citizens of a Smart City. The study has pointed out that the data gathered in Smart Cities is not only used to optimize life for its citizens and to stimulate economic development, it could also lead to politics of inclusion in which it is said how the individual should behave in relation to the population (s)he is being part of. Citizens mdo so because it is not always evident for the citizens where they are being observed and where the collected data is used for.

Another factor that influences the citizens of the Smart City is that data gathered in Smart Cities is not actually as objective and innocent as it looks like, because this data cannot exist without human interference. The institutions gathering the data, which are both public (governments) and private (i.e. Alibaba, Huawei), have the power to decide which information is used to build urban policies on (Schuilenburg & Peeters, 2018).

This imagined objectivity of the data and the ignorance towards this new forms of knowledge in the contemporary age, gives the government and the people deploying smart technologies the opportunity to describe their findings as objective and accountable. This might let people just take the knowledge as it is and forget about the socially constructed nature of it. Perhaps we can even say that in this form, the citizens are served a mirage, but in fact this can all lead to new forms of surveillance and directing towards normative behavior.  

References

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GOVInsider. (2018, October 17). Five Chinese smart cities leading the way | GovInsider. Retrieved December 5, 2018.

Hanell, L., & Salö, L. (2017). Nine Months of Entextualizations. In C. Kerfoot, & K. Hyltenstam (Eds.), Entangled Discourses (pp. 13–61). New York, United States: Taylor & Francis Group.

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Kitchin, R. (2015). Making sense of smart cities: addressing present shortcomings. Cambridge Journal of Regions, Economy and Society, 8(1), 131–136. 

Luckmann, T., & Berger, P. (1966). The foundations of knowledge in everyday life. In T. Luckmann, & P. Berger (Eds.), The Social Construction of Reality (pp. 13–61). New York, United States: Penguin Random House LLC.

Schuilenburg, M., & Peeters, R. (2018). Smart cities and the architecture of security: pastoral power and the scripted design of public space. City, Territory and Architecture, 5(13), 1–9.

Yu, W., & Xu, C. (2018). Developing Smart Cities in China. International Journal of Public Administration in the Digital Age, 5(3), 76–91.