AI in Latin America: Smart Cities or Surveillance States?

Artificial intelligence is reshaping urban landscapes across the globe, but in Latin America, its rollout raises a stark dilemma: will it foster vibrant, efficient smart cities or slide into dystopian surveillance states? With more than 80% of the region’s population concentrated in urban areas, cities are under immense pressure to tackle soaring crime rates and crumbling public services. Leaders often see AI as a quick win—a tool to restore order and modernize without the grind of deep-rooted reforms. Yet, this rush carries risks. Without strong safeguards, AI could widen inequalities and hand undue power to authorities, turning promise into peril.

The region’s $68.5 billion digital connectivity gap amplifies these challenges. Many Latin American countries depend on imported tech infrastructure, which fosters reliance on foreign vendors and off-the-shelf solutions. This makes it all too easy to adopt ready-made surveillance tools instead of crafting homegrown systems that respect human rights. When cities buy into AI rather than build it, they surrender not just technical know-how but also control over technologies that profoundly affect daily life. The real question isn’t if Latin America should adopt AI—it’s how to leverage its strengths while upholding democratic values, curbing misuse, and ensuring real gains for everyday people. Encouragingly, the region is already generating innovative examples that point the way forward.

Navigating this path demands attention to regulation. Latin America has a brief opportunity to shape AI governance tailored to its needs, avoiding the fate of becoming a mere adopter of rules from distant powers. The global scene splits between hands-off approaches in one corner and stringent controls in another, leaving room for the region to carve out a balanced “third way”—one that spurs innovation without sacrificing protections for rights and equity.

These regulatory needs stem from hands-on experiences across the region. From biased facial recognition in Brazil to pioneering legal AI in Argentina, Latin America has seen AI’s dual edges: its potential to empower democracy and its capacity for authoritarian overreach. Real-world cases show the region isn’t a blank slate; it holds valuable insights into successes, pitfalls, and the high costs of poor AI implementation.

The Urban AI Landscape in Latin America

Latin American cities stood at an AI tipping point long before advanced tools like large language models gained widespread attention. These efforts mirror the area’s intricate tech environment, blending influences from the United States, China, and growing local talent. Early adoptions included systems like Uruguay’s version of PredPol, sourced directly from U.S. developers, alongside China’s push to export surveillance gear throughout the region. At the same time, homegrown projects are gaining traction, signaling a shift toward self-reliance.

Take Argentina’s PROMETEA system, for instance. Developed by local prosecutors and tech experts, it automates everyday legal paperwork while ensuring humans retain oversight. In Chile, the MIRAI project adapts algorithms originally from MIT for breast cancer detection, fine-tuning them with regional health data to fit local realities. Colombia’s MAIIA platform, born from collaboration between the Inter-American Development Bank and domestic developers, highlights how partnerships can drive tailored solutions. This mix of influences underscores larger geopolitical dynamics, as Latin America balances external pressures with efforts to cultivate its own AI capabilities.

Urban AI isn’t just about gadgets; it’s about addressing core city struggles like traffic congestion, waste management, and public safety. In sprawling metropolises from Mexico City to São Paulo, AI promises data-driven fixes—think predictive analytics for bus routes or sensors for real-time pollution monitoring. But the devil lies in execution. When systems prioritize speed over scrutiny, they can exacerbate divides. Poorer neighborhoods, often underserved by infrastructure, might see AI deployed unevenly, benefiting elites while marginalizing others. This uneven rollout ties back to the digital gap, where rural-urban disparities mean not all citizens have equal access to the benefits—or risks—of these technologies.

Moreover, the region’s biodiversity and social diversity add unique layers. AI trained on global datasets might falter in diverse contexts, like recognizing indigenous languages or adapting to varied climates. Local innovations, therefore, aren’t just preferable; they’re essential for relevance and fairness.

Spotlight on Successes: Building AI That Serves People

Several initiatives stand out for their thoughtful integration of AI into urban life, demonstrating how technology can enhance services without eroding trust.

In Barranquilla, Colombia, the MAIIA platform uses AI to map informal settlements with an impressive 85% accuracy rate. This tool pinpoints exact locations where residents lack basic water access or where a new road could link isolated communities to job opportunities. Planners use these insights for precise, compassionate development—upgrading utilities in overlooked areas rather than imposing top-down plans. By visualizing data on interactive maps, MAIIA empowers officials to make informed choices that directly improve quality of life, reducing the guesswork in resource allocation.

Shifting to health, Santiago’s Proyecto MIRAI in Chile applies AI to mammogram analysis, forecasting breast cancer risk years ahead. This isn’t about replacing doctors; it’s about equipping them with early warnings based on local patient data. In a region where healthcare access varies widely, such tools can democratize prevention, catching issues sooner in under-resourced clinics. The project’s success lies in its hybrid approach: AI handles pattern recognition, but medical professionals interpret results, ensuring cultural and contextual sensitivity.

Even beyond city limits, AI is making waves in agriculture, a backbone of many Latin American economies. Argentina’s startup Kilimo deploys AI for smarter irrigation, helping farmers conserve a staggering 72 billion liters of water. This not only combats climate change effects like droughts but also bolsters food security and economic stability. By analyzing soil moisture and weather patterns in real time, Kilimo optimizes water use, showing how AI can extend urban smart tech principles to rural sustainability.

These examples illustrate a common thread: AI as a supportive tool, amplifying human efforts rather than supplanting them. They also highlight economic upsides—cost savings, efficiency gains—that make a compelling case for investment, provided safeguards are in place.

The Pitfalls: When AI Goes Awry

Not all stories are uplifting. High-profile missteps reveal the dangers of hasty or unchecked deployment, often amplifying biases or invading privacy.

Consider Rio de Janeiro’s 2019 pilot of AI-powered facial recognition for policing. Intended to streamline investigations, it backfired spectacularly. Watchdog reports noted rampant false positives, which derailed cases and led to wrongful detentions. In one notorious incident, authorities arrested the wrong woman for a murder, all because the system misidentified her. Worse, the technology disproportionately targeted Black communities—90% of those arrested via the pilot were Black, fueling accusations of systemic racism. These errors didn’t just waste resources; they eroded public confidence in law enforcement, turning a potential safety net into a source of injustice.

The shift in Buenos Aires from PROMETEA to ChatGPT offers another cautionary tale. PROMETEA, launched in 2017, transformed legal workflows by cutting document drafting from 90 minutes to just one, all while embedding human rights protections. Its framework rested on three pillars:

• Strict ethical codes for everyone involved in development and deployment.
• A narrow focus on routine tasks, leaving complex judgments to humans.
• Full transparency, with human review at every critical juncture.

Praised widely, it even inspired clones like Colombia’s PretorIA. Yet, courts recently began ditching it for ChatGPT, lured by promises of even quicker results. This swap discards a transparent, locally built system attuned to Argentine law for a foreign, commercial alternative. ChatGPT stores data overseas, shows higher error rates, operates opaquely as a “black box,” and lacks grasp of local nuances. It’s a classic case of chasing shiny shortcuts at the expense of control and accountability.

This trend underscores a key insight from research: specialized, smaller-scale AI models are often 10-30 times less expensive than massive general ones and perform better on niche, repetitive municipal tasks like legal drafting or urban mapping. Opting for giants like ChatGPT ignores these efficiencies, potentially locking cities into costly, less effective dependencies.

Other failures echo these issues. Uruguay’s PredPol system, meant to forecast crime hotspots, matched no better than old-school methods and was scrapped after three years. It highlighted the limits of imported predictive policing without local calibration—algorithms trained elsewhere couldn’t account for the region’s unique social dynamics.

Then there’s Argentina’s Salta province, which in 2017 teamed with Microsoft to predict teenage pregnancies “five or six years in advance—with the first and last name and address.” Shockingly, the project hid its data sources, assumptions, and follow-up strategies. “High-risk” girls faced invasive monitoring: agents snapped photos, tracked GPS, and surveilled movements. Critics slammed it as a tool to police women’s bodies rather than address root causes like education or healthcare access, blurring lines between prediction and control.

These cases aren’t isolated; they stem from common flaws: over-automation without oversight, secrecy that invites abuse, and a mindset viewing AI as a cure-all. In diverse Latin America, where inequality runs deep, such lapses can entrench discrimination, hitting marginalized groups hardest.

Crafting Intentional AI: A Human-Centered Approach

What sets thriving AI projects apart? A deliberate design philosophy that puts people first, treating technology as an enhancer of judgment, not a substitute.

Successful systems emphasize transparency, allowing users to trace and question AI logic. They bake in human rights from the start—think bias audits, privacy protocols, and inclusive training data—rather than patching ethics later. PROMETEA exemplifies this: even where it’s lingering, prosecutors hold the reins on final calls, ensuring AI aids without dominating.

Similarly, MAIIA in Colombia arms planners with data, not dictates. It maps vulnerabilities to guide humane interventions, like prioritizing water lines in favelas. Proyecto MIRAI pairs AI predictions with doctor expertise, focusing on risk alerts over diagnoses to avoid overreach in sensitive health matters.

Contrast this with flops, where automation reigns unchecked. Opaque operations shield flaws from view, while “quick-fix” hype skips rigorous testing. The result? Systems that falter under real-world scrutiny, wasting funds and trust.

To build better, Latin American cities should prioritize:

• Human oversight loops: Mandate reviews for high-stakes decisions.
• Ethical integration: Embed rights assessments in every phase, from data collection to deployment.
• Local adaptation: Train models on regional data to sidestep imported biases.

This approach isn’t anti-innovation; it’s about sustainable progress. By focusing on augmentation, AI can tackle urban woes—like optimizing public transport in Bogotá or monitoring floods in Lima—while safeguarding dignity.

Broader implications extend to workforce impacts. As AI handles rote tasks, it frees professionals for creative roles, but only if training programs keep pace. In Latin America, where youth unemployment bites, this could spark job growth in AI ethics, data curation, and system maintenance—fields ripe for regional expertise.

Forging Local Solutions for Democratic AI

A framework for democratic AI that protects liberties is within reach. Cities can act now with practical steps: enforce safeguards, run thorough pilots, and champion transparent, home-developed tech.

For starters, outright bans on risky tools like facial recognition or predictive policing aren’t technophobic—they’re democratic essentials. These technologies carry outsized abuse potential, especially in regions with histories of state overreach. Banning them creates breathing room for safer innovations, like community-based safety apps.

Pair this with radical transparency: Public registries of all AI in use, akin to proposals in Chile, would list systems, purposes, and risks. Citizens could then hold officials accountable, fostering trust.

No need to reinvent the wheel. Tools like formal Ethical Impact Assessments provide structured ways to weigh pros and cons before launch. Regional commitments, such as the Montevideo Declaration and the Roadmap for AI from the 2023 UNESCO summit, offer blueprints for ethical adoption. Initiatives like the fAIr LAC partnership through the Inter-American Development Bank enable knowledge-sharing, best-practice exchanges, and group negotiations with tech providers for fairer deals and stronger protections.

Economically, local focus pays off. Building in-house cuts long-term costs and builds skills, reducing the $68.5 billion connectivity chasm. Collaborations across borders—say, a shared AI hub for urban planning—could amplify this, turning dependency into leadership.

Challenges remain, from funding shortages to political will. Yet, successes like PROMETEA prove feasibility. Policymakers must prioritize equity: ensure AI bridges gaps, not widens them, by involving communities in design.

AI System	Country	Purpose	Key Success Factor	Outcome
PROMETEA	Argentina	Legal document automation	Human oversight & transparency	Time reduced 90 min to 1 min; inspired regional adaptations
MAIIA	Colombia	Informal settlement mapping	Local data integration	85% accuracy; targeted urban development
Proyecto MIRAI	Chile	Breast cancer risk prediction	Hybrid AI-medical expertise	Early risk detection tailored to local health
Kilimo	Argentina	Agricultural irrigation	Real-time environmental analysis	72 billion liters water saved; climate resilience
Rio Facial Recognition	Brazil	Policing	N/A (failure)	High false positives; 90% Black arrests; wrongful detentions
PredPol	Uruguay	Crime prediction	N/A (failure)	No better than traditional methods; discontinued after 3 years
Salta Pregnancy Prediction	Argentina	Social risk forecasting	N/A (failure)	Invasive surveillance; lack of transparency

This table compares deployments, highlighting patterns: successes thrive on localization and ethics, while failures ignore them.

“AI’s advance is inevitable in Latin America’s cities. Whether it builds inclusive smart cities or veers toward surveillance states hinges on the governance choices made today.”

Latin America’s future with AI rests on proactive policies. By drawing from these lessons—celebrating wins, learning from losses—the region can steer toward tech that unites and uplifts, not divides and dominates. The tools exist; the will must follow.