How far can smart city management go?

In 2030, the validity period of the Sustainable Development Goals (SDGs) defined in the 2030 Agenda approved by the United Nations General Assembly in 2015 comes to an end. An agenda in which cities not only have a specific SDG (number 11) but are also gaining increasing prominence in the advances (still, admittedly, insufficient) that are occurring in most of the remaining 16 SDGs.

Let's imagine that, by 2030, when the United Nations once again proposes global objectives for a new period in order to make this planet a more habitable and just place, Artificial Intelligence (AI) has advanced so much as to be capable of leading us towards that dreamed world. That our cities have specific algorithms thanks to which, once the objectives to be achieved are introduced, the appropriate management parameters for each case are defined.

The so-called generative AI, which consists of using AI to generate new designs or solutions, is indeed capable of analyzing, driven by advanced machine learning algorithms, large amounts of data to identify patterns and predict trends, which can help those who govern cities to improve emergency responses, mitigate extreme weather phenomena, allocate resources to infrastructure improvements, and facilitate the universal and efficient deployment of services.  

As with all emerging technology, more or less well-founded speculations about its possibilities skyrocket. In the case of urban management, these would be some of the areas on which we are beginning to have incipient applications, as was recently seen at the Smart City Expo World Congress.

1. Mobility: the capacity for massive data analysis, together with predictive algorithms, could prevent traffic congestion and optimize routes and frequencies of public transport. Something that will become even more present and essential as autonomous vehicles emerge on our streets.
2. Energy efficiency: through smart sensors and algorithms, usage patterns can be analyzed to regulate lighting, heating, and cooling in public and private buildings. Additionally, intelligent waste management uses sensors to monitor container capacity, optimizing collection routes.
3. Security: AI, through the integration of surveillance cameras and video analysis, can detect suspicious behavior patterns or identify emergency situations. Facial recognition systems improve surveillance and allow for rapid identification of individuals.
4. Urban planning: predictive models help anticipate population variations and infrastructure demands, facilitating informed decision-making. Optimization algorithms contribute to designing more efficient cities in terms of space and resources.
5. Citizen participation: AI not only has the potential to transform urban management from a public management perspective but also to empower citizens. The enablement of AI-based digital platforms facilitates community participation in decision-making, allowing for more informed intervention in urban development projects.

In a survey of 80 mayors from different cities around the world, more than three-quarters expressed interest in leveraging generative artificial intelligence to streamline administrative processes, improve data-driven decision-making, and enhance citizen participation.

However, the same study found that only a percentage less than 5% of cities are actually using this technology. In most cases, this was attributed to a lack of awareness and insufficient digital fluency and technical knowledge as obstacles to adoption. To provide greater knowledge and criteria in this regard, some institutions and cities, such as Boston, have developed guidelines for local government management staff.

One of the aspects that raises the most reluctance has to do with the ethical and privacy challenges that arise. Everyone is aware that the massive collection of data and the use of algorithms must be managed transparently and ethically to avoid possible abuses and discrimination in the exercise of government tasks. But putting this purpose into practice is not an easy task, especially when many of these data and algorithms are in the hands of private companies and can also be used for profit. Greater citizen involvement can influence ensuring that the benefits of technological innovation are distributed equitably.

Everyone is aware that the massive collection of data and the use of algorithms must be managed transparently and ethically to avoid possible abuses and discrimination in the exercise of government tasks

As always, the key is to build and defend shared values, as well as fundamental principles of action. These elements are usually clearly activated in the face of serious crises (as happened with the pandemic) but it is much harder for them to prevail when it comes to phenomena that are understood as future threats.

At this point, we must admit that, fortunately, the reconciliation of interests, conflict management, or the alignment of values and principles and, above all, the dream of the city of the future, including what the common objectives will be in terms of development and well-being and how to achieve them, are issues in which technology can accompany, but not replace, people.