In a recent post in NYUAD’s Room of Requirement group (a popular university forum for frequently asked questions) on Facebook, I found out about a tool called “Learning Analytics”. This tool embedded in the NYU Classes system provides “near real-time feedback for professors on what teaching methods are actually working on an individualized basis.”

If you haven’t heard about it yet, this system conducts clickstream, network and text analysis of how, when and the frequency at which students access and interact with study materials. This data is packaged into an interactive dashboard, “showing what each student is reading, watching, and working on at what time and in what order.” The goal of the system is to provide professors with continuous insight, allowing them to adjust their teaching methods to make use of the most effective materials.

Len Peters, Vice President and Chief Information Officer at NYU, argued that this data analysis system is a necessary tool “to improve instruction, reimagine courses, and boost learning outcomes for students.” Peters expressed that the role of NYU IT — and this system by extension — is “to explore new ways to leverage learning data to help improve outcomes on an individual level.” Peters said that, in the next few years, Learning Analytics will “shift to more open models of data collection, as faculty hope to get data about learning experiences from teaching and learning tools beyond the [Learning Management System].” The plan is to incorporate “AI capabilities into the dataset to enable faculty and advisors to see and predict patterns in learning.”

Image courtesy of King’s College London Learning Analytics

This system and the plans outlined by Peters draw heavy parallels to a current topic: smart cities. Smart cities are cities devised to “bring together infrastructure and technology to improve the quality of life of citizens and enhance their interactions with the urban environment.” In a similar fashion to NYU Classes’ Learning Analytics, these high tech urban developments track and collect data to manage assets, resources and services more efficiently.

For instance, data collected from citizens, devices and other sensors is processed and analyzed to monitor and manage traffic congestion and transportation systems, power plants, water supply networks, crime detection and many other community services.

The Lower Manhattan Security Initiative, for example, “is designed to detect threats and perform pre-operational terrorist surveillance south of Canal Street in Lower Manhattan. LMSI combines an increased police presence with technology to accomplish its mission.” The program began in Aug. 2008 with the goal of creating a central command center with access to surveillance feeds from more than 3,000 cameras and 100 license plate readers.

Image courtesy of Getty Images

The goal behind integrating these new technologies is to empower city officials to make informed decisions after collaborating directly with both community and city infrastructure. In short, the smart city allows the government to monitor what is happening in the city and how the city is evolving, reducing costs and consumption and bettering communication.

Both systems rely on technologically advanced processes to collect data while the user browses pages or roams streets, under the premise that the user experience will improve, eventually.

It seems like even though these systems have been designed with good intentions, there is a clear breach of the user’s privacy. Both NYU Classes and LMSI rely on incessant and involuntary data collection practices. The introduction of these surveillance systems raises concerns about our rights to privacy, data governance and the future use of the collected information. The right to privacy is commonly understood as one's right to preserve personal information from public scrutiny, although legally it varies depending on where one is located. However, the right to privacy is explicitly formulated under Article 12 of the 1948 Universal Declaration of Human Rights, which states the following:

“No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence … Everyone has the right to the protection of the law against such interference or attacks.”

Yet, there is no way of opting out of smart city surveillance or Learning Analytics. In the case of the smart city, data collected by traffic sensors can be used to suggest different routes to your destination, accounting for traffic jams and changes in public transportation. What if this data is used to push your commute towards using the services of a third-party company like Uber, Lyft or Careem?

Similarly, while a professor could be using the Learning Analytics system to understand where their students get lost, I fear a more Orwellian future. What if this professor uses the insights from Learning Analytics as a justification to penalize a student whom they deem hasn’t spent enough time reading or watching certain materials?

And the questions that are most concerning: Where is this data going? What policies govern the collection, analysis and storage of data? Is there a way for us to track who has access to and who engages with the data?

Throughout the next few weeks, I will write three more features articles in The Gazelle to explore the impact of smart city surveillance around the world, addressing the risks, proven benefits and future outcomes of the new urban phenomenon. The last article will contextualize the evolution of smart city surveillance in terms of life at the Saadiyat campus, understanding how these technologies affect our daily lives.