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In a discussion published on the new version of the EIP-SCC website, Graham Colclough (UrbanDNA) makes an attempt to answer a critical question for the ‘smart city’ market and yet, one that is quite impossible to get an easy answer to: How much does an urban data platform cost?
For the full article, click here.
What matters for us in this article are the definitions of ‘smart city’ concepts as used and understood within the European Innovation Partnership on Smart Cities and Communities (EIP-SCC), and in particular in its Action Cluster working on ‘Urban Platforms’.
Those definitions are being relayed in italics below and this article goes beyond them, to investigate the deeper costs (or societal challenges) that can be brought about by urban data platforms.
An ‘Urban Platform’ is …
… the implemented realisation of a loigcal architecture/design that brings together (we say “integrates”) data flows within and across city systems
… and exploits modern technologies (sensors, cloud services, mobile devices, analytics, social media etc)
… providing the building blocks that enable cities to rapidly shift from fragmented operations to include predictive effective operations, and novel ways of engaging and serving city stakeholders
… in order to transform, in a way that is tangible and measurable, outcomes at local level (e.g. increase energy efficiency, reduce traffic congestion and emissions, create (digital) innovation ecosystems, efficient city operations for administrations and services).
Why does Europe need harmonised standards for smart cities? Read the full interview with Dita Charanzová, a Czech MEP and vice-chair of the European Parliament’s Internal Market Committee, published on euractiv.com.
‘City Data’ is that which is held by any organisation – government, public sector, private sector or not-for-profit – which is providing a service or utility, or is occupying part of the city in a way that can be said to have a bearing on local populations and the functioning of that space.
This initial part of the definition brings the question of data ownership. Who owns the data collected in smart cities? What impact on citizens’ privacy? On this issue, the European Parliament published in September 2015 a study for the LIBE committee untitled ‘Big Data and Smart Devices and Their Impact on Privacy’.
It can be static, near-real time or in the future, real time, descriptive or operational.
Further, in the future, data will be to a greater extent generated by individual citizens and this too (with due consideration to privacy and a strong trust framework) can be considered city data.
What can cities do to protect privacy?
While acknowledging that urban data platforms are engines for more efficient urban governance (in the area of energy and mobility especially), good governance implies the adoption of a clear data management scheme, in line with EU rules.
In Europe, the General Data Protection Regulation (GDPR) is the new EU legal framework on data privacy and security which attempts to deal with these challenges, adopted in April 2016. A dedicated portal has been created to prepare all actors collecting, processing and storing data in Europe, and that of European citizens. Visit the GDPR portal at http://www.eugdpr.org/
Earlier this year, the Green Digital Charter (GuiDanCe project) organised a webinar on ‘Data management and citizens’ privacy in smart cities’ and open governance. The speakers were Daniel Sarasa (Zaragoza City Council) and Antonio Kung (EIP-SCC ‘Citizen Focus’ Action Cluster on the implementation of the GDPR).
. You can watch the recording at http://bit.ly/2omBDO1.
This article was originally published on CityLab.com (Richard Florida, 16 May 2017)
The panel was chaired by Peter Stone of University of Texas at Austin along with researchers from Rethink Robotics, Allen Institute for AI, Microsoft, and academics from Harvard, MIT, Johns Hopkins, Columbia, UC Berkeley, and other universities from around the world. Their study, Artificial Intelligence and Life in 2030, outlines the dramatic impact artificial intelligence (AI) is having and will continue to have for our cities and the way we live and work in them over the next couple of decades.
It outlines the implications of several key dimensions of AI, including:
- Large-scale learning or algorithms that crunch ever-larger datasets
- Deep learning procedures that recognize images, video, audio, speech, and language
- Reinforcement learning that shifts from pattern recognition to experience-driven decision-making
- Robotic devices that can physically interact with environments and people
- Computer vision that allows computers to see and perform tasks better than people
- Natural language processing that does more than react to requests—it communicates through speech
- Collaborative systems, crowdsourcing, and human computation
- Algorithms and computational tools that can apply economic and social data to realign incentives for people and businesses
- The “Internet of Things” that networks appliances, vehicles, buildings, and cameras
- Neuromorphic computing that mimics biological neural networks to improve the efficiency and robustness of computer systems
The report outlines what these technologies mean for cities and raises deep policy (and downright philosophical) questions about their impact across several areas of urban life. Here are a few thoughts reflecting on what this new technological might promise for cities.
Transportation—more than driverless cars
Everyone and their mother is talking about autonomous vehicles, or AVs, which are already being tested on the streets of several cities, including Pittsburgh. The potential relief from traffic congestion and the tragedy of human error on the road make this a top priority for the dream of personal transportation. But technical, economic, and ethical questions about our autonomous future abound—from the possible (major) glitch of pedestrian deaths to the potential job losses from automation to the possible fatal erosion of public transportation. We need to be ready for the next time the car transforms the city.
Artificial intelligence could also help systems be more dynamic. Real-time information, machine learning, and algorithms could turn public transportation into a much more vibrant public good, eliminating much of the current frustrations and frictions they generate now. AI could allow us to better allocate resources to make transportation more reliable and more equitable.
Public safety and privacy
Cities have already begun to deploy a wide variety of AI technologies for security purposes. Expect those trends to continue through to 2030. Analytics have successfully helped combat white collar crime, such as credit-card fraud, and could also prove useful in preventing cyber-crimes in the future. These technologies might not only help police departments solve crimes with less effort but also could assist crime prevention and prosecution by improving record keeping and automatically processing video for anomalies (including evidence of abusive policing).
But as we’ve seen with this kind of technology deployed for surveillance and predictive policing at the street level, the central question for cities is building trust and eliminating discriminatory targeting. The study argues that with proper research and resources, AI prediction tools could help remove or reduce human bias rather than reinforcing the current systemic problems. But these same powerful tools have a way of replicating the bias of the humans who create the technology in the first place. And techniques like network analysis, which can be used to disrupt criminal or terrorist plots, also have the potential for overreaching, threatening civil liberties, and violating the privacy of city residents.
Work and life
Artificial intelligence also portends major changes to health care, education, home care, and related services. AI may enable more efficient economic development of so called “low-resource communities” that have higher rates of poverty, joblessness, and therefore have limited funds for public programs and infrastructure. With data mining leading incentives and priorities, there’s promise to the idea that AI might unburden systems with limited resources and allocate resources better. Algorithms could connect restaurants to food banks to turn excess in to resources or connect the unemployed to jobs, for example. Harnessing social networks could also help distribute health-related information and address homelessness.
Predictive models could not only help government agencies put limited budgets to better use, they could produce more complex thinking to anticipate future problems rather than reacting to a crisis such as the lead poisoning in Flint. After a crisis hits, AI might assist in allocating resources, say by identifying children at risk of exposure or finding women who are pregnant that might need prenatal care to mitigate adverse birth outcomes.
A key caveat would be to make sure these tools act as a guard against discriminatory behavior—identifying people for services without baking racial indicators or proxy factors into the machine learning of these systems.
The way forward
AI brings a contradictory future to our cities. On the hand, tech-optimists see technology like autonomous vehicles, mobile healthcare, and robot teachers freeing us from mundane chores like commuting and waiting in doctor’s offices and making our cities better, more inclusive and sustainable places. On the other hand, techno-pessimists see a dystopian future where AI and robots take away jobs and we live in a state of perpetual surveillance.
The report takes a more measured approach. “AI will likely replace tasks rather than jobs in the near term, and will also create new kinds of jobs,” the authors state. “But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will likely be lost.”
The study highlights a need for a new set of strategies and policies to guide the use of AI in the city, spanning legality and liability, certifications, agency control, innovation and privacy, labor and taxation. It also calls for more research, training and funding for cities and local governments to better understand and be ready for this coming revolution.
AI presents a complex set of considerations for cities. As with any big new technology, the possibilities are exciting—but mayors, policy makers, and urbanists must be vigilant to ensure that we set in place the regulations and institutions required to make the most of these new technologies while minimizing their downsides.