**5. The Symbiosis: Towards an Artificially Intelligent City?**

AI is one of the most powerful and disruptive technologies of our time, and its influence on urban settlements and activities is growing rapidly, ultimately affecting everyday life [121,122]. Given that cities are the main hubs and drivers of most socioeconomic activities, political actions, and environmental transformations, it is important to understand how the development of AI and the development of the city are intertwining [123]. This brings up the question of whether there is or could be a symbiotic relationship between them, and if this revolutionary technology could offer novel sustainability solutions feeding into new urban models. After all, AI has already entered our cities, and it is therefore essential to critically examine and question its urban sustainability potential [15].

A study by Yigitcanlar et. al. [124] investigated these questions through a thorough systematic literature review—99 peer-reviewed research articles concentrating on both smart cities and AI. The study arranged the findings under four smart city domains, as shown in Figure 2—i.e., economy, society, environment, governance.

In terms of the 'economy' domain of smart cities, the AI focus is predominately on technological innovation, and business productivity, profitability and management. Some of the most typical contributions of AI to this domain include [124]:


In terms of the 'society' domain of smart cities, the AI focus is predominately on the public health, wellbeing, and education areas. The COVID-19 pandemic is particularly accelerating the use of AI in these areas. The main contributions of AI to this domain include [124]:


In terms of the 'environment' domain of smart cities, the AI focus is predominately on the transport, energy, land use, and climate areas. Some of the key contributions of AI to this domain include [124]:


• Predicting the risks of climate change via machine learning algorithms combined with climate models—employed to foresee potential disastrous events in specific geographical areas and act in advance.

Moreover, beyond urban environmental issues, AI is also being used for addressing planetary environmental challenges. Overall, as Vinuesa et al. [104] have argued, AI applications can potentially contribute to achieving 17 Sustainable Development Goals (SDGs). Below, we provide a summary of the application areas touched by AI technologies, specifically in relation to environmental sustainability.


In terms of the 'governance' domain of smart cities, the AI focus is predominately on national and public security, urban governance and decision-making in government. Some of the principal contributions of AI to this domain include [124]:


Nonetheless, the above list of benefits should not obscure that of the many problems that AI is bringing. AI is a double-edged sword. This sentient sword can be used to fight against global sustainability issues, but it can also cause much collateral damage as well as harm those who wield it. The drawbacks of AI are equal to its potentials [143]. Below, we provide a summary of prospects and constraints of AI according to different smart city domains [144]. As pointed out earlier, we need more than *technology* to achieve urban sustainability. Particularly *policy* and *community*, which are the other two drivers of smart and sustainable cities (see Figure 2), should be refined and operationalized to neutralize the technological shortcomings of AI.

• On the one hand, the *prospects* of AI in the *economy* domain include: enhancing productivity and innovation, reducing costs and increasing resources, supporting the decision-making process, automating decision-making [145–147]. On the other hand, the *constraints* of AI involve: making biased decisions, having an unstable job market, losing revenue streams and employment, and generating economic inequality [148–150].


The above prospects and constraints should be evaluated in relation to the five different levels of autonomy that characterize the decision-making power of AI [15,169]. Level 0 corresponds to no autonomy—meaning full human control on every decision. Levels 1 and 2 correspond to assisted decision-making, where in Level 2 AI offers moderate assistance or recommendation. In Level 3, decisions require human approval, whilst in Level 4 only human monitoring or human oversight is needed, to step in in case of a problem. Level 5 is equal to complete autonomy, meaning that decisions are taken by an AI in an unsupervised manner. As we progress to Level 5, both the magnitude of disruption and opportunity will become greater. With this greater power, AI will have to assume greater responsibility, and it will be thus crucial to develop 'responsible and ethical AI' before we get to Level 5 [170–172]. From an urban point of view, AI technology is progressing fast, thereby gaining more and more autonomy in cities. Especially in experimental cities, where the pace of technological innovation is usually rapid, we can already see parts of the built environment that are not *automated* but rather *autonomous*.

The key difference between *automation* and *autonomy* is that an automated technology repetitively follows patterns previously established by a human intelligence, while an autonomous technology establishes its own patterns, seldom repeating the exact same action [15]. Simply put, this is the difference between an elevator always going up or down stopping at invariable floors, and an autonomous car which can traverse entire cities and never follow the same route twice. The difference is critical because autonomous AIs operate in real-life environments where the life of real people is at risk. Not in a confined elevator shaft but in, for example, an urban road shared by hundreds of individuals. Here unsupervised, AIs have to make important decisions and take actions that can actually kill. This is the case of the first pedestrian fatality caused by an autonomous car in Tempe (Arizona) in March 2018. An autonomous Uber was incapable of dealing with the uncertainty that is typical of unconfined urban spaces, and its incapacity killed a woman that was crossing a road outside the designated crossing lane [173]. The greater the autonomy of AI is, the greater its constraints are, given that, to date, we do now have urban artificial intelligences that can fully understand what is right or wrong (the issue of ethics) and then answer for their behavior (the issue of responsibility).

Furthermore, it is important to recognize that both the fields of smart and sustainable cities and AI are in constant evolution. As Sections 3 and 4 have illustrated, numerous smart-city projects have been implemented and an even larger number is under development, while the evolution of AI has reached only two levels out of four. This means that we have seen only a small part of what smart urbanism

companies.

disadvantaged districts.

and AI can potentially offer. Whether the best or the worst is yet to come, is an open question. For sure, at the moment there is neither an ideal AI system, nor an ideal smart and sustainable city that can serve as a universal model of development and, given the many geographical differences that exist in the world, the very idea of having a global paradigm is questionable in the first place [68,174,175]. This is to say that we need to continue researching both conceptualizations and practical applications of AI and smart and sustainable cities, across geographical spaces and scales [176]. Only then will we be able to analyze and fully evaluate the symbiosis between AI and the city and understand whether this can give birth in particular places to 'artificially intelligent cities' [144].

Lastly, there is the critical issue of how we define and construct artificially intelligent cities. In its current conceptualization, an *artificially intelligent city* "is a city where algorithms are the dominant decision-makers and arbitrators of governance protocols—the rules and frameworks that enable humans and organizations to interact, from traffic lights to tax structures—and where humans might have limited say in the choices presented to them for any given interaction" [177]. For such type of cities to achieve a condition of sustainability, the issues of transparency, fairness, ethics, and the preservation of human values need to be carefully considered. These unresolved issues are intrinsic to AI and thus hinder its sustainability. In other words, in order to improve the chances that the city of artificial intelligence becomes a sustainable city, we need better AI, and this will be the topic of the next section.
