Latin American Cities in the Age of AI: Navigating the Technological Revolution

Artificial Intelligence (AI) is transforming cities. It is redefining how governments plan, manage, and police them. It is also reshaping the circumstances under which citizens work, move, and engage in civic life. The long-term impacts of AI in urban environments are still uncertain. However, transformations will likely be profound and enduring as these technologies advance and permeate diverse facets of our lives.

AI has the traits of general-purpose technologies, which are defined by their significant impact on productivity¹, widespread application across industries, improvement over time, and generation of complementary innovations^2,3. Classic examples are the steam engine, electricity, and information and communications technologies. Through their impact on economic, social, and physical structures, these technologies drastically transform cities and how we live in them.

As Latin Americans, we inhabit one of the world’s most urbanized regions. Considering the remarkable advancements in AI that we are currently witnessing; it is essential to reflect on the benefits and risks these technologies bring to our cities and conceive mechanisms to manage them. This article discusses a few of the multiple potentially disrupting impacts of AI in cities and explores strategies for navigating change.

AI and the Future of Work

Over the last few months, we have seen outstanding progress in generative AI, spearheaded by the release and exponential adoption of Chat GPT. The chatbot achieved the milestone of 100 million monthly active users within the first two months of its release, making it the fastest-growing application in history⁴. This wave of breakthroughs is creating immense expectations for its capacity to unleash productivity gains. Yet, it is also causing great apprehension over its potential for widespread work automation. According to the last paper released by Open AI, 19% of jobs in the US have at least half of their tasks exposed to AI automation⁵. Meanwhile, a recent study by Goldman Sachs estimates that 18% of jobs globally could be automated by AI, with Latin American economies facing a higher risk than the global average⁶.

While there is still significant uncertainty about the likelihood and timing of the impacts of AI in the labor market, these technologies threaten to erode the intrinsically urban base of white-collar jobs⁷. The studies mentioned above have found that occupations that require tertiary education are particularly vulnerable to automation. Specifically, traditional administrative, legal, sales, finance, and design jobs have been identified as being at the highest risk. Should these predictions materialize, advancements in AI could have regressive effects on income distribution both between and within cities.

On the one hand, capitals and large dynamic cities where talent and corporate headquarters are concentrated, such as São Paulo, Santiago, or Mexico City, will likely attract most of the new high-paying AI-connected positions. By contrast, smaller and less sophisticated metropolitan areas might be unable to attract lucrative jobs. On the other hand, the AI rollout could harm income distribution within cities if a large share of the white-collar workers displaced by automation move into lower-paying roles, hollowing the middle class.

The realization of these scenarios would further reinforce the already high levels of inequality that characterize Latin American cities. From a spatial perspective, the interplay of these factors could exacerbate the stark residential segregation of our cities. Furthermore, it could trigger new inter and extra-regional migration patterns, which are hard to predict now.

The social and economic impacts of work automation will depend on multiple factors. While many of these are beyond government control (i.e., the pace of innovation), policymakers have multiple leavers at their disposal. The early conception and implementation of regulations and safety nets can mitigate the technological transition’s distributive effects. Numerous best practices have been learned from the policy response to the social and economic consequences of the COVID-19 pandemic, including cash transfer schemes, upskilling programs, rent subsidies, and eviction and utility shutoff moratoriums⁹. All of these could help to temporarily mitigate the adverse effects that automation might have on households’ incomes. However, AI experts warn that structural policies will be required for alleviating the distributive impacts of technological change in the long run. Notably, Sam Altman, CEO of OpenAI, is a vocal proponent of implementing Universal Basic Income (UBI) schemes¹⁰. Altman’s argument highlights the importance of UBI in the coming years for safeguarding individuals from the potential decline in salaries resulting from labor market disruptions triggered by AI.

AI and Surveillance

In recent years, the adoption of AI-powered surveillance systems has rapidly increased in Latin America. Argentina, Brazil, Colombia, Ecuador, Mexico, Paraguay, and Peru already have ongoing deployments of facial recognition software¹¹. Considering the high levels of violent crime that characterize many cities in Latam, the allure of these technologies is evident. Nonetheless, the deployment of AI surveillance technologies in the region has been carried out on weak legal grounds, without proper human rights assessments, and has been characterized by its lack of transparency¹¹.

There are two main reasons why the unrestrained rollout of AI surveillance is troubling. First, these technologies have built-in algorithmic and data biases^12,13. Commercial facial recognition software, for example, exhibits significant gender and race disparities in its accuracy. Notably, these technologies are particularly inaccurate in the case of dark-skinned individuals and women¹⁴. Moreover, algorithms trained on arrest data can exacerbate pre-existing racial biases in law enforcement¹⁵. Secondly, without proper safeguards, facial recognition technologies can be a major threat to the rights to privacy, freedom of expression, and peaceful assembly¹¹. A notable illustration of these concerns is the recent judicial investigation which uncovered that Buenos Aires’ surveillance system was unlawfully utilized for obtaining data about journalists, politicians, activists, union leaders, and business people¹⁶. Evidently, in the hands of authoritarian-leaning leaders, the misuse of these technologies could put our democracies in peril¹⁷.

Whether facial recognition has a place in democratic societies is still hotly contested. However, it’s essential that its use is anchored in human rights in countries where it is already deployed. In this regard, city-level regulations can play a pivotal role in federal systems, by enacting more stringent conditions for the use of AI than those set at the national level¹¹. Some measures which can help minimize the risks to human rights associated with facial recognition surveillance systems include: limiting their use to exceptional cases and requiring judicial authorization, establishing robust oversight and accountability mechanisms, and mandating the disclosure of data about the features and performance of the technologies in use¹¹.

Navigating technological change

AI will continue to permeate and transform cities. It will allow governments to improve citizen engagement, reduce tax fraud, and optimize energy, mobility, and waste management systems, among other benefits¹⁸. It will also spark significant productivity gains in the private sector. However, as illustrated above, these benefits come with associated risks. The following guiding principles, derived from the literature and the cases discussed above, can serve as a useful starting point for policymakers in addressing the opportunities and challenges presented by the AI revolution in Latin American cities.

1. Fairness and equity: AI systems should not discriminate based on personal characteristics^19,20.
2. Transparency and accountability: Governments should be open about the AI systems adopted, the nature of their use, and their performance^19,20. A best practice in this dimension, pioneered by Helsinki and Amsterdam, is building algorithm registries where information about the technologies in use is disclosed^21,22.
3. Privacy and security: Adequate safeguards should be adopted to guarantee individual rights and data protection²³.
4. Participation: Community engagement should be considered in cases where AI can impact access to opportunities, rights, or safety¹⁹.
5. Social Protection: Policies to ensure the well-being of households affected by job automation should be devised.
6. Cooperation: Collaboration between municipalities is critical for adapting AI technologies, which come predominantly from the Global North and China, to address local challenges²⁶_.
7. Proactivity: Cities should adopt a proactive rather than reactive approach to technological change^15,19. Integrating AI considerations into strategic planning is a best practice in this dimension¹⁸. Other key measures include devising policies aimed at building talent, developing digital infrastructure, fostering R&D, and collecting relevant data ^24,25.

The impacts of AI on our economies, societies, and democracies will critically depend on the nature and timing of governments’ responses to technological change. Integrating the principles outlined above into public policies is an initial step towards harnessing the benefits that AI technologies offer, while mitigating their potential negative impacts on equity, human rights, and freedoms. Nevertheless, it is imperative to continue to expand and refine these, as AI continues to advance, and its impacts become more tangible and better understood.

       References

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4. UBS (2023). Let’s chat about ChatGPT. UBS Wealth Management.
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