3. AI use cases in LAC governments

Previous OECD work and research from other organisations have identified a number of key areas where governments are focusing their real-world use of AI in the public sector.

Over the last year, governments worldwide have rapidly deployed innovative solutions in response to the COVID-19 crisis,1 many of which employ AI to deliver tailored solutions and messaging for citizens and residents to assist their pandemic response (OECD, 2020[1]) (OECD, 2020[2]).

Beyond immediate needs in times of crisis, the most common and immediate uses of AI in the public sector involve automating simple tasks and guiding decisions to make government more efficient and informed (Ubaldi et al., 2019[3]), (Partnership for Public Service/IBM Center for the Business of Government, 2019[4]). Governments have also used AI strategically in a variety of ways to enhance their relationships with and services for citizens and residents (Berryhill et al., 2019[5]).

Globally, a number of topic-specific use cases have emerged in key application areas for AI in the public sector. In particular, many public sector efforts have been concentrated on public safety and security, enhancing regulatory functions, healthcare and transportation (Ubaldi et al., 2019[3]). Governments have also used AI to address cross-cutting issues, such as the Sustainable Development Goals (SDGs) (Berryhill et al., 2019[5]) (IDIA, 2019[6]).

Research for this report found that AI usage in LAC countries is generally aligned with global patterns. However, examination of LAC country AI efforts also found numerous use cases related to enhancing public integrity and accountability, and improving education. This trend accords with two regional priorities: preventing corruption and reducing school dropouts.2 These efforts are noteworthy because they demonstrate a stronger focus on these areas than observed by the OECD in other regions and countries in regard to AI in the public sector.

This chapter explores a non-exhaustive set of real-world projects that fall under the observed themes. In particular, it explores LAC public sector AI projects in the areas presented in Figure 3.1.

Before the world was even aware of the threat posed by COVID-19, AI systems had detected the outbreak of an unknown type of pneumonia in China. Countries are now using AI tools to help monitor and predict the spread of COVID-19 in real time, to enable rapid diagnosis, and to search for treatments at an unprecedented pace and scale (OECD, 2020[7]). One of the most evident outcomes of the innovative response of governments to the pandemic was the rapid acceleration of digital innovation and transformation (OECD, 2020[8]). Throughout the crisis, AI technologies and tools were employed to support the efforts of policy makers, the medical community and society at large to manage every stage of the pandemic and its aftermath (OECD, 2020[7]). In particular, governments used AI to:

  • Understand the virus and accelerate medical research on drugs and treatments

  • Detect and diagnose the virus, and predict its evolution.

  • Assist in preventing or slowing the spread of the virus through surveillance and contact tracing.

  • Respond to the health crisis through personalised information and learning.

  • Monitor the recovery and improve early warning tools.

LAC governments are also employing or developing the use of AI in a variety of ways that match and reinforce these themes (see Box 3.1).

In the context of government, an important and immediately achievable benefit of AI is improvement in the way that public servants perform their tasks. AI has the potential to help government shift from low-value to high-value work and focus more on core responsibilities by “reducing or eliminating repetitive tasks, revealing new insights from data … and enhancing agencies’ ability to achieve their missions” (Partnership for Public Service/IBM Center for the Business of Government, 2019[4]).

The average civil public spends up to 30% of their time on documenting information and other basic administrative tasks (Eggers, Schatsky and Viechnicki, 2017[9]). Automating or otherwise avoiding even a fraction of these tasks would enable governments to save a tremendous amount of money and re-orient the work of public servants around more valuable tasks, resulting in more engaging jobs and a greater focus on people (Partnership for Public Service/IBM Center for the Business of Government, 2019[4]).

The growing interest in AI is driven by the vast and increasing amount of available data. However, large volumes of data can also hinder governments from extracting useful knowledge, a phenomenon referred to as “information overload” (Speier, Valacich and Vessey, 1999[10]). AI can help governments overcome information overload, gain new insights and generate predictions to help them make better policy decisions. In Argentina, for example, the Prometea system has reduced operation times in the justice service, leading to replication in institutions inside and outside the country. The use of robots to automate repetitive tasks can also help governments improve efficiency by reducing the processing time of certain public services. Examples of these uses of AI can be found in Box 3.2.

Moving beyond the automation of repetitive tasks, cases such as the prediction of judgments in lawsuits against the State in Colombia show that AI can also improve efficiency by providing richer analyses for enhanced decision making (Box 3.3). The PretorIA case, presented in the same box, provides an example of how public institutions can interact with civil society, listen to key concerns about the implementation of AI and making the necessary adaptations to the technology. This case highlights the importance of a vigilant and capable civil society, with capacities to collaborate with the public sector in the co-creation of trustworthy digital public services.

In addition to using AI to address specific topics, governments are also utilising AI applications in a variety of ways to engage with citizens, residents and businesses. One popular type of AI used in the public and private sectors, especially in the exploratory stages, is chatbots. Simple chatbots use a rules-based approach to interact with citizens in order to perform functions such as answer frequently asked questions. More sophisticated versions leverage machine learning to undertake more complex, less concrete interactions, as illustrated by the case of Jaque on the digital platform of the State of Alagoas, Brazil (Box 3.4).

AI can also be used to provide simpler and more tailored services for citizens and businesses. For instance, the Commercial Opportunities Map in Argentina and ParaEmpleo in Paraguay employ algorithms to scan multiple data sources and compare them against user needs and characteristics in order to produce better recommendations. AI technologies have also been used by governments to better comprehend the opinions and perspectives of their citizens at scales that were previously unfeasible. This is achieved through the use of Natural Language Processing and clustering techniques to gain valuable insights from vast troves of information (Berryhill et al., 2019[5]). Querido Diário in Brazil is a project that flips these roles and allows citizens to gain deeper understanding of the information published by the state in official newspapers. Finally, the Colombian Government created a project to enhance its relationship with farmers by optimising soil analyses and providing tailored recommendations for soil fertilisation. These examples are discussed in Box 3.5.

Public safety and security is one of the main focus areas for governments exploring the use of AI. It encompasses both physical safety security and cybersecurity, and can cover a broad swath of topics for which governments are responsible including law enforcement, disaster prevention and recovery, and military and national defence. The State of the Art in the Use of Emerging Technologies in the Public Sector paper notes, for instance, that “in the field of surveillance, computer vision and natural language processing systems can process large amounts of images, texts and speeches, to detect possible threats to public safety and order in real time” (Ubaldi et al., 2019[3]).

The OECD could find no instances in which LAC governments are actively using AI to support cybersecurity efforts. However, Uruguay does appear to be advancing towards this area in the form of its “aiUTEChallenge” Cybersecurity Strengthening Program, which is exploring ways to apply AI in combination to monitoring, detection and response to incidents, and digital identification, among others. The country expects to make concrete developments in these areas in the near future.3

While the use of AI for cybersecurity remains light, there are many use cases concentrated in law enforcement and other efforts related to the criminal justice system. As a broad example, the International Criminal Police Organization (INTERPOL), of which all countries in the scope of this review are members,4 is using different types of AI systems for law enforcement and has published Artificial Intelligence and Robotics for Law Enforcement,5 which explores the potential of AI for policing and details real-world projects already underway. Predictive AI systems in particular have gained popularity in the region (see the examples in Box 3.6), often among local urban governments. As can be seen in the examples, AI systems in this area may have some utility, but they also often operate in grey areas and introduce ethical dilemmas that governments must fully consider and evaluate. Transparency of uses and processes and explainability of algorithms become key elements to engage stakeholders in detecting risks of unfair treatment and finding alternative solutions. Additionally, as illustrated by the case of Predpol in Uruguay, governments should also consider that using AI may not always be the best solution to a problem and recognize that other technologies can have similar effects at lower costs.

Another security area where AI is increasingly applied globally is surveillance. Facial recognition has been used in a number of cities around the world to help locate suspected criminals and counter terrorism (Berryhill et al., 2019[5]), although the practice can be highly controversial. LAC governments do not appear to make significant use of facial recognition AI systems; however, the practice is growing in the region, as is civil society resistance (Arroyo, 2020[15]). In some LAC countries, experimental usage is underway to use AI to analyse facial imagery along with other video, imagery and audio (e.g. voices) for the detection of criminal activity. The cases of the Command, Control, Communications and Computing Centre (C4) in Bogotá (Colombia) and ECU 911 in Ecuador (Box 3.7) highlight two main challenges governments need to overcome in order to generate trust in the these systems: establishing the necessary safeguards when processing sensitive personal data (e.g. biometric data) to prevent unfair treatment of historically discriminated groups; and defining clear frameworks for the use of these technologies in order to prevent possible abuses such as the profiling and persecution of political opponents or protesters.

As can be seen in these examples, LAC governments like other governments around the world must be cautious in exploring the use of AI in this field and should leverage this technology in ways that do not undermine public trust or tread on civil liberties. Governments need to balance the tensions of using AI systems (e.g. those using data harvesting and monitoring) to serve the public interest, with inevitable concerns about “big brother” and risks of infringing on freedoms and rights. Chapter 4 on Efforts to develop a responsible, trustworthy and human-centric approach, and the OECD Report Embracing Innovation in Government: Global Trends 2020 – Public Provider versus Big Brother6 (OECD, 2020[16]) provide some guidance and considerations that governments should consider as they explore AI for public safety, security and other purposes.

Regulation refers to the diverse set of instruments through which governments set requirements for enterprises and citizens. Regulation includes all laws, formal and informal orders, subordinate rules, administrative formalities and rules issued by non-governmental or self-regulatory bodies to whom governments have delegated regulatory powers (OECD, 2018[17]).7

While regulations and other types of rulemaking often target individuals and organisations outside of the public sector, AI provides significant opportunities to increase government capacity to improve the design and delivery of regulations and regulatory enforcement activities (OECD, 2019[18]) (OECD, 2019[19]). For instance:

  • Regulators could apply machine learning tools to the vast quantities of data available to them in order to help predict where they should focus their regulatory efforts. Such tools could be used to determine which key areas and enterprises and citizens merit investigation and inspection.

  • Machine learning can be used to better predict the outcome of likely litigation, ensuring greater cohesion between the views of the courts and the views of regulators.

Such potential could enable regulators to streamline their operations by allowing them to move resources away from wasteful activities – such as investigating businesses that are likely compliant with the law, or proceeding with litigation that has a good chance of being unsuccessful – towards activities that better achieve their regulatory goals. Box 3.8 discusses three examples of the use of AI to improve public sector regulatory functions, mainly through increased process efficiency.

Beyond its applications for COVID-19 response, AI is used across the healthcare sector in numerous ways, with enormous potential for government in countries that have national health services. AI applications, especially those involving machine learning, can help interpret results and suggest diagnoses, and predict risk factors to help introduce preventative measures (Ubaldi et al., 2019[3]). They can also suggest treatments and help doctors create highly individualised treatment plans. Combined with the knowledge of doctors and other medical experts, AI can lead to better accuracy, higher efficiency and more positive outcomes in the health field (see Box 3.9).

One of the most widely publicised applications of AI is autonomous vehicles, such as the self-driving cars being tested by Uber and several major motor companies. While the government certainly has a role to play in regulating and understanding the implications of such vehicles, they seem to present less opportunities for public sector innovation. Beyond such vehicles, governments around the world and in LAC countries are using AI to transform the ways in which they predict and manage traffic flows (Box 3.10). While all of the overarching themes that the OECD has observed globally also appear to be areas of focus for LAC governments, transportation perhaps had the weakest representation in terms of observed initiatives.

The adoption of the 2030 Agenda for Sustainable Development saw nations worldwide commit to a set of universal, integrated and transformational goals and targets, known as the Sustainable Development Goals (SDGs). The 17 goals and 169 targets represent a collective responsibility and a shared vision for the world. Governments are working to make progress to reach them by 2030, with many exploring the potential of AI to help achieve this objective.

Research by McKinsey Global Institute has identified a non-comprehensive set of about 160 cases that demonstrate how AI can be used for the “non-commercial benefit of society” (MGI, 2018[23]). Of these, 135 touch on one of the 17 SDGs. These cases often take the form of private sector initiatives, or partnerships among the private sector, public sector and/or civil society. The ECHO initiative (Box 3.11), led by the United Nations Population Fund in partnership with local governments in Colombia, illustrates how AI can be used to support the SDGs at large.

With regard to support for individual focus areas, LAC countries are using AI as a tool to address air pollution, a common threat in a several cities and regions (see Box 3.12 for examples). Such efforts are related to SDGs 3 (good health and well-being) and 11 (sustainable cities and communities), and indicate solid progress in working towards the 2030 goals, as well as potential opportunities to explore the use of AI in supporting other SDGs. Peru’s draft national AI strategy includes a specific objective to develop AI systems aligned with the SDGs, although these are not yet in place.

One of the most dynamic focus areas in LAC is the application of digital technologies to improve transparency and accountability over the use of public resources. Governments are using AI as a tool to determine patterns of action of public and private actors, detect risks and vulnerabilities in public contracting, and cross-reference sources of information for better auditing and public transparency. Although this category could be considered an expression of improving government efficiency, the strength of emphasis in the LAC region demonstrates the importance of the fight against corruption in the region.

Corruption and the mismanagement of public resources is a top concern in LAC countries, with perceptions of corruption on average higher in Latin America than in most regions (OECD, 2018[24]). According to the Global Corruption Barometer for Latin America and the Caribbean 2019, 65% of people in Latin America and the Caribbean think their government is run by and for a few private interests.8 This contributes to an overall lack of trust in the government. The share of the LAC population having little or no trust at all in governments reached 75% in 2017, 20 percentage points higher than in 2010. The most crucial determinant to tackle this issue is strengthening public integrity (OECD, 2018[24]).

In line with the OECD Recommendation on Public Integrity,9 the use cases presented in Box 3.13 address different areas of opportunity to improve public integrity in the region while also increasing the efficiency of public resources.

A particular focus area for AI in the LAC region is education – especially preventing school dropouts. Although this issue relates to SDG 4 (quality education), the level of attention at the regional level makes it a trend worthy of a separate discussion. Education was also highlighted as a key theme at the AI Latin America SumMIT, where the participants agreed that AI could become a catalyst for change in the educational system. AI has the potential to modify ways of teaching and contribute to better follow-up of students through more personalised learning processes (Anllo et al., 2021). This growing interest in applying AI to education is directly linked to the issue of school dropouts. Only 60% of students complete secondary education, although it is compulsory in most countries of the region.10 Additionally, 36% of young women who drop out of school do so due to pregnancy or maternal care, while economic reasons tend to be the main cause of school dropout among young men.

In order to address this issue, (Josephson, Francis and Jayaram, 2018[25]) recommend the use of early warning information systems in programmes and schools to identify risk situations in a timely manner, and to enable targeted and relevant interventions. Most of the use cases presented in Box 3.14 are aligned with this recommendation, specifically the use of AI to help prioritise at-risk children who may need special assistance or guidance. However, such profiling activities are not without risk. One of the first public sector applications of AI in the LAC region took the form of a system to predict teenage pregnancy and school dropout in the province of Salta (Argentina); however, concerns were raised about the possible reproduction of bias and the existence of unfair or discriminatory treatment. Considering ethical standards and principles throughout the development life cycle of an AI system is therefore crucial to delivering trustworthy, inclusive and safe AI systems. In addition, this case shows that diverse and multi-disciplinary development teams can deliver more informed, effective and tailored solutions. Other examples in Box 3.14 relate to upskilling and increasing the efficiency of public education processes.

All of these use cases demonstrate growing interest among LAC governments in exploring the potential of AI in the public sector. As is common with other regions and countries around the world, many of the current uses uncovered represent early-stage pilots or implemented AI systems that tend to use simple but proven techniques. Several of them, though, demonstrate a growing level of sophistication in terms of techniques and machine learning algorithms. This is likely to continue as a number of LAC governments seek to achieve the goals laid out in their national AI strategies, while others work to develop their own. This growing desire to harness the opportunities presented by AI and the increasing sophistication in terms of what LAC governments seek to achieve with the technology also bring with it a number of challenges to overcome and responsibilities to meet. As can be seen in these examples, some LAC governments are already encountering ethical dilemmas and the civil society backlash that can occur as new approaches are pursued. The OECD promotes public sector experimentation and the adoption of AI when it is done in a trustworthy and ethical manner, and with the right investments and enablers in place needed to achieve successful, sustainable results. The next chapter provides guidance on how LAC governments can achieve this, and the extent to which such enablers are already in place in the region.


[15] Arroyo, V. (2020), Instead of banning facial recognition, some governments in Latin America want to make it official, https://www.accessnow.org/facial-recognition-latin-america/ (accessed on 18 February 2021).

[5] Berryhill, J. et al. (2019), “Hello, World: Artificial intelligence and its use in the public sector”, OECD Working Papers on Public Governance, No. 36, OECD Publishing, Paris, https://dx.doi.org/10.1787/726fd39d-en.

[27] CAF (2018), El alto costo del abandono escolar en América Latina, https://www.caf.com/es/conocimiento/visiones/2018/08/el-alto-costo-del-abandono-escolar-en-america-latina/ (accessed on 15 April 2021).

[21] Consejería Presidencial para Asuntos Económicos y Transformación Digital (2020), Proyectos de transformación digital, trámites y servicios para el ciudadano.

[9] Eggers, W., D. Schatsky and P. Viechnicki (2017), AI-Augmented Government: Using cognitive technologies to redesign public sector work, Deloitte University Press, https://www2.deloitte.com/content/dam/insights/us/articles/3832_AI-augmented-government/DUP_AI-augmented-government.pdf.

[11] Giandana, F. and D. Morar (2019), “Victor Frankenstein’s responsibility? Determining AI legal liability in Latin America”, Global Information Society Watch 2019: Artificial intelligence: Human rights, social justice and development, pp. 168-171, https://giswatch.org/sites/default/files/gisw2019_artificial_intelligence.pdf.

[12] Gómez Mont, C. et al. (2020), Artificial Intelligence for Social Good in Latin America and the Caribbean, https://publications.iadb.org/publications/english/document/Artificial-Intelligence-for-Social-Good-in-Latin-America-and-the-Caribbean-The-Regional-Landscape-and-12-Country-Snapshots.pdf.

[6] IDIA (2019), Artificial Development in International Development: A Discussion Paper, International Development Innovation Alliance/AI & Development Working, https://static1.squarespace.com/static/5b156e3bf2e6b10bb0788609/t/5e1f0a37e723f0468c1a77c8/1579092542334/AI+and+international+Development_FNL.pdf.

[25] Josephson, K., R. Francis and S. Jayaram (2018), Promoting secondary school retention in Latin America and the Caribbean, http://scioteca.caf.com/handle/123456789/1248.

[13] López, J. and J. Castañeda (2020), Automatización, tecnologías digitales y justicia social: la experimentación con la pobreza en Colombia, CETyS Universidad de San Andrés., https://guia.ai/wp-content/uploads/2020/05/Lopez-Casta%C3%B1eda-Automatizacion-tecnologias-digitales-y-justicia-social-la-experimentacion-con-la-pobreza-en-Colombia.pdf (accessed on 18 February 2021).

[22] Martinho-Truswell, E. et al. (2018), Towards an AI Strategy in Mexico: Harnessing the AI Revolution, http://go.wizeline.com/rs/571-SRN-279/images/Towards-an-AI-strategy-in-Mexico.pdf.

[23] MGI (2018), Notes from the AI Frontier: Applying AI for Social Good, McKinsey Global Institute, https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Applying%20artificial%20intelligence%20for%20social%20good/MGI-Applying-AI-for-social-good-Discussion-paper-Dec-2018.ashx.

[8] OECD (2020), Embracing Innovation in Government: Global Trends 2020 - Innovative Responses to the COVID-19 Crisis, OECD Publishing, https://trends.oecd-opsi.org/trend-reports/innovative-covid-19-solutions/.

[16] OECD (2020), Embracing Innovation in Government: Global Trends 2020 - Public Provider versus Big Brother, OECD Publishing, https://trends.oecd-opsi.org/trend-reports/public-provider-versus-big-brother/.

[7] OECD (2020), OECD Digital Economy Outlook 2020, OECD Publishing, Paris, https://dx.doi.org/10.1787/bb167041-en.

[1] OECD (2020), The Covid-19 Crisis: A catalyst for government transformation, OECD Publishing, http://www.oecd.org/coronavirus/policy-responses/the-covid-19-crisis-a-catalyst-for-government-transformation-1d0c0788/.

[2] OECD (2020), Using artificial intelligence to help combat COVID-19, OECD Publishing, https://www.oecd.org/coronavirus/policy-responses/using-artificial-intelligence-to-help-combat-covid-19-ae4c5c21.

[19] OECD (2019), Going Digital: Shaping Policies, Improving Lives, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264312012-en.

[18] OECD (2019), “Using digital technologies to improve the design and enforcement of public policies”, OECD Digital Economy Papers, No. 274, OECD Publishing, Paris, https://dx.doi.org/10.1787/99b9ba70-en.

[20] OECD (2018), Digital Government Review of Brazil: Towards the Digital Transformation of the Public Sector, OECD Digital Government Studies, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264307636-en.

[24] OECD (2018), Integrity for Good Governance in Latin America and the Caribbean: From Commitments to Action, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264201866-en.

[17] OECD (2018), OECD Regulatory Policy Outlook 2018, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264303072-en.

[14] Ortiz Freuler, J. and C. Iglesias (2018), Algorithms and Artificial Intelligence in Latin America: A Study of Implementation by Governments in Argentina and Uruguay, http://webfoundation.org/docs/2018/09/WF_AI-in-LA_Report_Screen_AW.pdf.

[4] Partnership for Public Service/IBM Center for the Business of Government (2019), More than Meets AI Part II, https://www.businessofgovernment.org/sites/default/files/More Than Meets AI Part II_0.pdf.

[10] Speier, C., J. Valacich and I. Vessey (1999), The influence of task interruption on individual decision making: An information overload perspective, pp. 337-360, https://doi.org/10.1111/j.1540-5915.1999.tb01613.x.

[3] Ubaldi, B. et al. (2019), “State of the art in the use of emerging technologies in the public sector”, OECD Working Papers on Public Governance, No. 31, OECD Publishing, Paris, https://dx.doi.org/10.1787/932780bc-en.

[26] World Wide Web Foundation (2018), Algorithms and Artificial Intelligence in Latin America: A Study of Implementation by Governments in Argentina and Uruguay, World Wide Web Foundation, http://webfoundation.org/docs/2018/09/WF_AI-in-LA_Report_Screen_AW.pdf.


← 1. The OECD OPSI report on Innovative Responses to the COVID-19 Crisis, which forms part of the Embracing Innovation in Government: Global Trends 2020 report series, provides an in-depth discussion on this topic. See https://oe.cd/c19-innovation.

← 2. The OECD report Integrity for Good Governance in Latin America and the Caribbean found that Latin America is perceived to have a higher level of corruption than most regions (OECD, 2018[24]). Moreover, only 60% of students complete their studies in the region, even though secondary education is compulsory in most LAC countries (CAF, 2018[27]).

← 3. See www.gub.uy/agencia-gobierno-electronico-sociedad-informacion-conocimiento/comunicacion/noticias/inteligencia-artificial-ciberseguridad for more information.

← 4. www.interpol.int/en/Who-we-are/Member-countries.

← 5. www.unicri.it/news/article/Artificial_Intelligence_Robotics_Report.

← 6. https://trends.oecd-opsi.org/trend-reports/public-provider-versus-big-brother.

← 7. The OECD Public Governance Directorate and its Regulatory Policy Division work to help governments achieve their missions through the use of regulations, laws and other instruments to deliver better social and economic outcomes and enhance the life of citizens and businesses. Their work can be found at http://oecd.org/gov/regulatory-policy.

← 8. www.transparency.org/en/news/political-integrity-lacking-in-latin-america-and-the-caribbean-especially-a.

← 9. www.oecd.org/gov/ethics/recommendation-public-integrity.

← 10. www.caf.com/es/conocimiento/visiones/2018/08/el-alto-costo-del-abandono-escolar-en-america-latina.

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