All novelty is but oblivion: Problem framing and AI regulation in Latin American policymaking

INTRODUCTION

“Salomon saith: There is no new thing upon the earth. So that as Plato had an imagination, that all knowledge was but remembrance; so Salomon giveth his sentence, that all novelty is but oblivion.”
Francis Bacon, The Essays

The rapid diffusion of Artificial Intelligence (AI) has intensified global debates over how emerging technologies should be governed. Across Latin America, governments, legislators, regulatory agencies, companies, and civil society organizations have increasingly sought to define appropriate institutional responses to AI-related risks and opportunities. National strategies, legislative proposals, administrative guidelines, and sectoral reforms have proliferated throughout the region, reflecting growing concerns over issues such as algorithmic discrimination, opacity, data governance, automation, and digital rights. Yet these initiatives differ considerably across countries, not only in their regulatory design but also in the ways policymakers conceptualize the nature of AI itself.

Some policy initiatives portray AI as an unprecedented technological disruption requiring specialized institutions, novel legal categories, and exceptional oversight mechanisms. Others approach AI primarily as an extension or amplification of pre-existing digital governance challenges, particularly those associated with data protection, privacy, platform regulation, and cybersecurity. These contrasting interpretations shape how regulatory problems are constructed, which risks become politically visible, and what kinds of policy responses are considered legitimate or feasible.

This article examines these emerging interpretive differences through a comparative analysis of AI governance debates in Argentina, Brazil, Chile, and Mexico. Rather than evaluating the effectiveness of regulation or predicting future regulatory outcomes, the article focuses on how AI is framed within policymaking processes and how these framings relate to different regulatory orientations. More specifically, it explores whether AI is predominantly constructed as a form of technological novelty demanding institutional innovation, or as a phenomenon that can be addressed through the adaptation of existing legal and bureaucratic frameworks.

The article draws on scholarship in public policy and political analysis that emphasizes the constitutive role of problem framing in governance processes. As Rochefort and Cobb (1994) and Rein and Schön (1993) argue, policy problems do not emerge as self-evident realities; they are interpreted, narrated, and politically constructed through competing categories and causal assumptions. Different framings privilege certain risks, actors, and institutional responses while marginalizing others. In the field of technology governance, framing processes are particularly consequential because emerging technologies are characterized by uncertainty, contested meanings, and rapidly evolving social expectations.

This perspective is especially relevant in debates surrounding AI governance. AI can be framed as a matter of innovation and competitiveness, as a human rights concern, as a security issue, or as a continuation of existing digital governance problems. Each interpretation tends to privilege different regulatory repertoires and institutional arrangements. Framing AI as a disruptive technological rupture may encourage the creation of new regulatory authorities, comprehensive legislative frameworks, or exceptional oversight mechanisms. By contrast, framing AI as continuity may favor incremental adaptation through pre-existing legal instruments and administrative structures.

To explore these dynamics, the article also considers the role of institutional legacies in shaping contemporary governance debates. In Latin America, data protection laws constituted one of the first major regulatory responses to the social and economic transformations associated with digitalization and internet expansion. Throughout the late 1990s and 2000s, countries across the region adopted legal frameworks addressing personal data processing, privacy rights, and digital accountability, often influenced by European regulatory models. These earlier experiences established legal concepts, bureaucratic structures, and governance repertoires that continue to inform contemporary debates on AI regulation.

The relationship between data protection and AI governance is particularly important because many current concerns surrounding AI (including automated decision-making, profiling, surveillance, and algorithmic bias) emerge directly from the large-scale collection and processing of personal data. Existing data protection regimes therefore provide both institutional precedents and interpretive frameworks through which governments may approach AI-related regulation. Some countries extend these inherited frameworks into the AI domain, while others seek to construct AI as a sufficiently distinct phenomenon requiring new regulatory architectures.

Latin America offers a productive setting for examining these dynamics. Although the region shares broad legal traditions and exposure to transnational governance models, countries differ significantly in state capacity, institutional development, regulatory traditions, and political priorities. Argentina, Brazil, Chile, and Mexico illustrate this variation particularly clearly. Brazil has advanced comparatively comprehensive discussions surrounding AI governance and institutional oversight. Chile combines administrative modernization with sector-specific reforms, particularly in criminal law. Argentina has pursued fragmented but rights-oriented initiatives linked to existing digital governance traditions. Mexico, meanwhile, exhibits a highly dispersed set of legislative proposals focused on sectoral and risk-specific concerns.

By comparing these cases, the article develops an exploratory interpretive typology of AI regulatory framing in Latin American policymaking. Rather than proposing a deterministic explanation of regulatory outcomes, the analysis identifies recurring interpretive tendencies through which policymakers construct AI governance problems and justify different forms of intervention. The article argues that AI governance debates in the region tend to cluster around three broad orientations: normalization, exceptionalization, and hybridization. These categories do not represent fixed or exhaustive models, but rather predominant interpretive patterns emerging across policy documents, legislative initiatives, and governmental strategies.

The article contributes to the literature in two ways. First, it contributes to discussions on technology governance in the Global South by showing that AI regulation is not shaped exclusively by technical considerations or external regulatory diffusion, but that it can also be (in)directly influenced by historically situated interpretations of technological change. Second, it contributes to scholarship on framing and digital governance by exploring how inherited regulatory repertoires may influence the ways emerging technologies are politically constructed and institutionally managed.

Methodologically, the article adopts an exploratory comparative interpretive approach based on qualitative analysis of official regulatory and policy documents produced between 1999 and 2024. The analysis focuses on how policymakers define AI-related problems, characterize technological risks, and justify regulatory responses across the four selected countries. Particular attention is given to the relationship between contemporary AI initiatives and earlier data protection frameworks.

The remainder of the article proceeds as follows. The next section reviews the literature on framing, technological governance, and institutional legacies. The following section outlines the methodological approach and document selection strategy. The empirical sections then examine the trajectories of Argentina, Brazil, Chile, and Mexico, identifying how different regulatory orientations emerge within each case. The discussion section compares cross-country patterns and develops the article’s interpretive typology. The conclusion reflects on the implications of these findings for the study of AI governance and digital regulation in Latin America.

FRAMING, NOVELTY, AND REGULATORY TRAJECTORIES IN TECHNOLOGICAL GOVERNANCE

Problem framing and technology governance

Public policy scholars have long argued that policies do not emerge in response to objective or self-evident problems, but rather through processes of interpretation and problem definition. As Bacchi (2009) argues, policies implicitly construct the problems they claim to address by privileging certain assumptions, risks, and causal narratives over others. In this sense, problem framing is not merely rhetorical: it shapes which issues become politically visible, which actors are considered responsible, and which solutions appear feasible or legitimate.

Interpretive approaches to policymaking emphasize precisely this dimension. Stone (1989) shows that policy debates depend heavily on symbolic representations, causal stories, and strategic narratives that organize political attention. Similarly, Hajer (1995) argues that policy controversies are structured through discursive coalitions that stabilize particular interpretations of social problems. Rather than treating regulation as a purely technical response, these approaches understand governance as a process through which institutions construct meaning under conditions of uncertainty and conflict.

This perspective has particular relevance for technology governance. Emerging technologies are rarely interpreted in stable or uniform ways. Artificial intelligence, for example, may simultaneously be framed as an innovation opportunity, a productivity tool, a human rights challenge, a security risk, or a source of economic dependency. These interpretive differences matter because they influence how governments prioritize risks and organize regulatory responses.

Scholarship in science and technology studies (STS) further highlights the role of collective imaginaries in shaping governance trajectories. Jasanoff and Kim (2015) describe “sociotechnical imaginaries” as publicly shared visions of desirable technological futures that become embedded in institutions and policy agendas. In the context of AI governance, imaginaries of modernization, competitiveness, automation, or digital sovereignty “actively shape institutional agendas, funding priorities, and regulatory pathways” (Oldenburg and Papyshev, 2025) by embedding specific views of tropes such as risk, responsibility, and progress into governance structures.

Rather than assuming that technologies possess intrinsic political meanings, interpretive approaches stress what might be termed interpretive flexibility: the idea that technologies can be understood through multiple, partially overlapping narratives depending on institutional context and political priorities. This article adopts that perspective. The central question is therefore not whether AI is objectively “novel,” but how policymakers construct and mobilize this putative novelty in regulatory documents.

Novelty, continuity, and the governance of emerging technologies

Historically, each new media or technology has been portrayed as revolutionary before eventually being normalized under existing frameworks. Early examples include the telephone, radio, and television. Marvin’s (1988) classic study shows that the telephone in the late 19th century was framed in popular discourse as a social disruptor (arousing fears of privacy invasion and moral decay) as well as a marvel of progress. Over time, however, these “new media” were integrated into legal and social norms. Winston (2002) famously argues that the notion of an “Information Revolution” is largely an illusion – digital communications are part of a longer social and technical evolution rather than a radical break. In his view, successive innovations followed similar patterns of scientific ideation, prototyping, and “intervening social necessity,” with any early hype or panic eventually giving way to normal diffusion.

Many scholars have documented a recurring cycle: initial moral panic or enthusiastic hype about a technology, followed by gradual normalization. Douglas (1986) and others note that each new medium (from radio and TV to the Internet) has triggered repeated moral anxieties (e.g. about juvenile delinquency or mass manipulation) which later subside as society adapts. Boczkowski (2005) also observes that while online news was once heralded as a disruptive leap, it ultimately evolved to fit into older journalistic institutions. In short, newness is often socially constructed. The Social Construction of Technology (SCOT) approach (Bijker, Hughes & Pinch, 1994) emphasizes that what counts as “new” or “disruptive” arises through social negotiation – technologies embody diverse, sometimes conflicting meanings for different groups. Scholars like Hård and Jamison (2013) further argue that technological innovation reflects shifts in broader social imaginaries, not intrinsic leaps in capability. Under this view, the so-called novelty of a technology depends on how society chooses to frame and repurpose it; in other words, “novelty” is not simply a property of technology itself but also a political and interpretive construction.

This insight is particularly relevant for AI governance: contemporary debates frequently portray AI as an unprecedented challenge requiring entirely new institutional responses. Yet several scholars caution against treating AI as wholly detached from earlier histories of automation, data extraction, and algorithmic management. Crawford (2021), for instance, emphasizes continuities between AI systems and longer-standing infrastructures of labor exploitation, surveillance, and resource extraction. Similarly, Elish and Boyd (2018) introduce the concept of a “moral crumple zone”, showing how framing AI as autonomous allows human operators to absorb blame for failures. This concept illustrates how portraying AI as cutting-edge can be used rhetorically to displace responsibility from systems designers onto the nearest human. Accordingly, the language of novelty can either highlight genuine institutional change or cloak continuities and protect existing power structures, and whether a technology is treated as fundamentally new or as business-as-usual often depends on who benefits from each framing.

These perspectives do not deny that AI may introduce important regulatory challenges. Rather, they insinuate that governance debates are shaped by competing interpretations regarding the extent to which AI should be treated as exceptional or as an extension of existing institutional concerns. In practice, many regulatory discussions combine both tendencies: AI is often framed as technologically transformative while also being incorporated into already familiar vocabularies of privacy, discrimination, administrative accountability, or consumer protection.

Institutional legacies and regulatory adaptation

Institutions develop trajectories that constrain future change (Mahoney, 2000; Pierson, 2000). Early decisions create sunk costs, legal entrenchments, and shared expectations that make radical departure difficult. In practice, this means that existing regulatory regimes tend to persist and be adapted to new problems rather than discarded wholesale. For example, many governments respond to digital and AI issues by extending pre-existing frameworks: telecom rules may be applied to digital networks, or data privacy laws updated for algorithmic profiling. These established regimes act as “paths” that guide policy: once resources and rules are in place, decision‐makers are biased toward incremental adjustments.

In the realm of technology policy, such path dependence is evident. Scholars of digital governance (i.e. Margetts & Dunleavy, 2013) argue that states often meet the first waves of innovation with familiar institutional tools (regulatory agencies, standards bodies) before eventually creating specialized offices or laws. In Latin America, this dynamic is pronounced: many countries have long-standing laws on data protection, broadcasting, and telecommunications that frame newer discussions about AI and big data. Aguerre and Galperin (2015) point out that Latin American Internet policy often mimics global models but is filtered through these local legal traditions. Likewise, Hilbert (2020) observes that infrastructural and institutional gaps in the region have led to both ambitious digital agendas and challenges in enforcement.

Importantly, path dependence is not fate. Existing governance frameworks may provide repertoires, vocabularies, and organizational models that policymakers selectively extend into new domains. Nonetheless, as Capoccia & Kelemen (2007) highlight, “critical junctures” (moments of crisis or opportunity when established paths can be broken or redirected) might take place. In technology policy, a critical juncture might occur when an unprecedented tech scandal or a major election leads to reform, rather than sticking to the status quo. Thus, while previous regulatory choices strongly influence current options, novel frames and contexts can enable significant change.

In Latin America, these institutional legacies are especially relevant because many countries entered AI debates with pre-existing trajectories in data protection, digital administration, and telecommunications reform. Earlier regulatory experiences established legal concepts, oversight practices, and normative vocabularies that continue to influence contemporary policy discussions. At the same time, countries in the region display important differences in institutional capacity, political coordination, and regulatory ambition, producing diverse combinations of continuity and innovation.

This perspective helps explain why similar global AI governance narratives may generate distinct regulatory responses across countries. Governments are exposed to many of the same international influences, including OECD principles, UNESCO recommendations, European regulatory debates, and private-sector discourse surrounding innovation and competitiveness. Yet these ideas are interpreted and operationalized through locally situated institutional contexts.

Regulatory uncertainty and emerging technologies

AI governance also raises questions concerning regulatory timing and uncertainty. Emerging technologies are difficult to regulate because their social impacts, technical trajectories, and institutional implications remain partially uncertain during early stages of diffusion. This creates tensions between anticipatory governance and regulatory caution.

Genus and Stirling (2018), drawing on the broader literature surrounding the Collingridge dilemma, note that technological governance often confronts a structural problem: technologies are easier to regulate before they become deeply entrenched, but their consequences are also less predictable at that stage. Conversely, once technologies become socially embedded, governance interventions may become politically and economically more difficult.

This tension appears frequently in contemporary AI debates. Regulatory actors often express concern about the speed of technological change and the possibility of institutional lag. At the same time, policymakers face uncertainty regarding which governance models will remain viable in rapidly evolving technological environments. As a result, many governments experiment with flexible or adaptive regulatory approaches, including ethical guidelines, soft-law instruments, sectoral frameworks, regulatory sandboxes, and risk-based governance models.

Global responses to AI illustrate this diversity. The European Union has taken a risk-based approach (sometimes called “precautionary”), focusing on regulating specific high-risk applications (Floridi, 2020; Kop, 2021). For example, the EU’s proposed AI Act would classify systems by risk level, imposing stricter rules on things like biometric surveillance. Scholars like Veale and Edwards (2018) critique this in terms of a “right to explanation” for AI, pointing out Europe’s caution. In contrast, the current United States administration has prioritized innovation-first, aiming to remove barriers to AI deployment. As reported by Shine and Bregman (2025), the July 2025 “AI Action Plan” explicitly frames AI policy around “accelerating AI innovation” by cutting “red tape” and working in partnership with industry. This plan rescinds prior rules seen as burdensome and even suggests using federal funding as leverage against states that impose stricter AI regulations.

Meanwhile, international bodies have offered principle-based frameworks. The OECD and UNESCO, for example, have endorsed high-level AI principles (Cath, 2018) emphasizing transparency, fairness, and human rights. These are softer yet globally recognized norms that can influence policy (for a more detailed and comprehensive view on the topic of global AI governance, see Maas, 2025). 

In the Global South, commentators like Gurumurthy and Chami (2019) argue for contextualizing these frameworks: technology governance must consider development goals, digital inequality, and sovereignty. Milan and Treré (2019) go further, proposing a “data justice” perspective that reframes governance around issues of equity and democratic control, not just economic growth. In sum, regulatory systems are diversifying: some blend old and new rules, others create novel institutions (AI task forces, multi-stakeholder councils), and many experiment with governance techniques designed for uncertainty.

It follows from these discussions that AI governance should not be understood as a linear transition from old regulatory models to entirely new institutional architectures. Rather, governance emerges through ongoing processes of interpretation, adaptation, and negotiation under conditions of uncertainty. This article approaches AI governance from that perspective, examining how policymakers construct AI as either continuity, novelty, or some combination of both, and how these framings shape emerging regulatory trajectories.

METHODOLOGY

This article adopts an exploratory comparative interpretive approach based on qualitative analysis of regulatory and policy documents related to artificial intelligence (AI) governance in Latin America. Rather than evaluating the effectiveness of regulation or establishing causal relationships between policy framing and regulatory outcomes, the study seeks to identify recurring interpretive tendencies through which policymakers construct AI-related governance problems and justify different forms of institutional intervention.

The analysis focuses on four countries: Argentina, Brazil, Chile, and Mexico. These cases were selected for three principal reasons. First, they represent influential actors in Latin American debates on digital governance and regulatory modernization. Second, all four countries possess prior trajectories in data protection governance, allowing comparison between earlier regulatory responses to digitalization and current AI-related initiatives. Third, each country has experienced recent legislative or policy activity concerning AI between 2023 and 2024, enabling analysis of contemporary governance debates within a shared temporal context.

The comparative design combines both cross-national and historical dimensions. On the one hand, the article compares how the four selected countries define AI-related problems and articulate regulatory responses. On the other hand, it examines how contemporary AI governance debates relate to earlier experiences in data protection regulation. This dual comparison makes it possible to analyze whether AI is predominantly framed through continuity-oriented approaches grounded in inherited regulatory repertoires, or through novelty-oriented approaches emphasizing institutional innovation and exceptional governance mechanisms.

The analysis is based on a corpus of official regulatory and policy documents produced between 1999 and 2024. These include data protection laws, national AI strategies, legislative proposals, administrative guidelines, regulatory opinions, and related institutional documents. The corpus was selected because these materials constitute the most formalized and publicly authoritative articulations of AI governance debates within each country. Although official documents do not capture the entirety of political negotiation surrounding AI governance, they provide a replicable and analytically coherent basis for examining how regulatory problems are publicly constructed and institutionally framed.

The historical dimension of the corpus centers on data protection frameworks. These laws were selected because they represented one of the earliest major regulatory responses to the expansion of digital technologies and internet-based data processing in Latin America. Throughout the late 1990s and 2000s, governments across the region confronted emerging concerns regarding personal data circulation, privacy, surveillance, and platformization. In doing so, they established legal concepts, administrative structures, and regulatory repertoires that continue to shape contemporary digital governance debates.

Data protection also constitutes a particularly relevant antecedent for AI governance because many contemporary concerns surrounding AI (including profiling, automated decision-making, algorithmic discrimination, and data extraction) emerge directly from large-scale data processing practices. Earlier data protection frameworks therefore provide an institutional and interpretive baseline through which current AI-related initiatives may be understood.

The historical corpus includes Argentina’s Personal Data Protection Act (Act No. 25.326, 2000) and subsequent regulatory decrees; Brazil’s General Data Protection Law (Lei Geral de Proteção de Dados Pessoais, LGPD) and the institutional development of the National Data Protection Authority (ANPD); Chile’s Law No. 19.628 on the Protection of Private Life and its subsequent reforms; and Mexico’s Federal Law on the Protection of Personal Data Held by Private Parties.

The contemporary corpus includes national AI strategies, administrative regulations, legislative proposals, and institutional opinions related to AI governance produced between 2019 and 2024. For Argentina, this includes the National AI Plan (ArgenIA, 2019)^[1], Disposición 2/2023 concerning trustworthy AI in the public sector, and multiple legislative initiatives addressing AI governance and oversight mechanisms. For Brazil, the corpus includes the National AI Strategy (2021), Bill 2338/2023, and opinions issued by the ANPD. In Chile, the analysis considers the National AI Policy (2021), bills, and legislative reforms concerning AI-related criminal offenses. In Mexico, the corpus includes multiple legislative proposals concerning AI-generated fraud, sexual violence, copyright, labor rights, healthcare applications, and broader regulatory initiatives. All documents were collected from official legislative portals, government repositories, and public institutional sources.

The analysis focuses on interpretive framing. In this article, framing refers to the recurrent interpretive patterns through which policy documents characterize AI-related risks, define governance problems, and justify institutional responses. Particular attention is given to how documents describe technological novelty, institutional adequacy, regulatory urgency, and the relationship between AI governance and pre-existing legal frameworks.

To structure the comparative analysis, the article employs a set of analytical dimensions that identify different ways in which regulatory documents frame AI through narratives of continuity and novelty, as summarized in Table 1. These dimensions are not intended as mutually exclusive classificatory categories, nor as fixed ideal types into which countries can be neatly assigned. Rather, they function as heuristic sensitivities that help trace how policymakers construct technological change, institutional adequacy, risk, and regulatory intervention across different contexts.

The analytical purpose is therefore interpretive rather than taxonomic. The study does not seek to determine whether a country follows a purely “continuity-oriented” or “novelty-oriented” model of governance. Instead, it examines how these interpretive repertoires coexist, overlap, or shift within policy debates and regulatory proposals. In practice, many documents simultaneously portray AI as an extension of pre-existing governance challenges and as a technologically distinctive phenomenon requiring adapted or innovative institutional responses.

Accordingly, since cases were not classified through binary assignment, but according to the predominance of interpretive tendencies identified across policy documents and legislative initiatives, the analytical dimensions presented in Table 1 should be understood as flexible interpretive lenses designed to guide comparative reading across cases, rather than as rigid variables intended to produce definitive classifications.

Table 1. Analytical dimensions of regulatory framings

Analytical dimension	Indicators frequently associated with continuity framings	Indicators frequently associated with novelty framings
Definition of AI	Extension of existing digital governance challenges. AI is framed as intensifying already familiar problems—such as privacy violations, discrimination, misinformation, or administrative opacity—rather than creating entirely new categories of harm.	Unprecedented technological phenomenon. AI is described as a fundamentally new technological development whose autonomy, scale, or unpredictability exceeds the capacities of existing legal and institutional frameworks.
Dominant policy concern	Adaptation and risk management. Regulatory responses focus on updating current rules and governance tools in order to manage emerging risks without substantially transforming institutional structures.	Institutional innovation and systemic transformation. Policymakers propose broad reorganizations of governance systems, including new regulatory paradigms, oversight models, or institutional architectures specifically designed for AI.
Regulatory logic	Incremental modification of existing frameworks. Governments respond to AI by introducing targeted reforms, amendments, or extensions to already existing laws rather than adopting comprehensive standalone legislation.	Creation of specialized governance mechanisms. New advisory bodies, observatories, ethical councils, certification systems, or technical committees are proposed to address AI-related issues specifically.
Institutional orientation	Reliance on existing agencies and legal repertoires. Existing institutions—such as data protection authorities, consumer protection agencies, or criminal courts—are treated as sufficiently capable of addressing AI-related challenges	New oversight bodies and AI-specific institutions. Policymakers advocate for dedicated AI regulators, supervisory authorities, or autonomous agencies with exclusive competence over algorithmic systems and AI governance.
Regulatory language	Integration, adaptation, extension. AI governance is incorporated into broader digital governance agendas, emphasizing continuity with existing principles, institutions, and regulatory traditions.	Disruption, autonomy, exceptional risk. AI is portrayed as generating novel forms of uncertainty, systemic risk, or societal disruption that justify exceptional regulatory responses and anticipatory intervention.

The comparative strategy seeks to balance similarity and variation across cases. Argentina, Brazil, Chile, and Mexico share broad civil-law traditions, democratic institutional frameworks, and exposure to transnational governance models such as the GDPR, OECD recommendations, and UNESCO principles (Del Campo, 2025; Del Campo et al., 2025). At the same time, they differ substantially in institutional capacity, regulatory density, bureaucratic development, and approaches to digital governance. This combination makes the selected cases suitable for examining how similar external pressures may produce different interpretive and institutional responses.

Several limitations should nevertheless be acknowledged. First, AI governance in Latin America remains an evolving and unstable field, meaning that many regulatory initiatives analyzed here are recent, incomplete, or still under legislative discussion. The article therefore examines emerging governance orientations rather than consolidated regulatory regimes. Second, the exclusive focus on official documents necessarily underrepresents informal negotiations, lobbying dynamics, corporate influence, and implementation practices. Third, the study does not seek to reconstruct policymaker intentions or establish direct causal relationships between framing and regulatory outcomes. Instead, it focuses on how governance problems are publicly articulated and institutionally represented within official policy debates.

Finally, limiting the analysis to four countries and to the relationship between data protection and AI governance inevitably excludes other potentially relevant trajectories, including cybersecurity, telecommunications, platform governance, and digital economy regulation. Nevertheless, this narrower focus enhances analytical comparability and allows closer examination of how inherited digital governance repertoires shape contemporary interpretations of AI regulation in Latin America.

FINDINGS

Argentina. Argentina was one of the earliest Latin American countries to adopt a comprehensive personal data protection framework. Law No. 25.326, enacted in 2000, consolidated a rights-based approach to informational privacy strongly influenced by the European Union’s 1995 Data Protection Directive while––at the same time––grounded in Argentina’s constitutional tradition of habeas data and judicial protection of privacy rights. The law established legal categories concerning “datos personales” (“información de cualquier tipo referida a personas físicas o de existencia ideal determinadas o determinables”), and “datos sensibles,” including information related to “origen racial y étnico, opiniones políticas, convicciones religiosas, filosóficas o morales, afiliación sindical e información referente a la salud o a la vida sexual,” while recognizing individuals’ rights to access, rectification, updating, and deletion of personal information.

The legislation framed digital governance problems primarily through concerns regarding opacity, informational asymmetry, and unlawful processing of personal data by both state and private actors. The law repeatedly emphasized the need to protect individuals against “tratamiento de datos” capable of generating discrimination or unjustified intrusion into private life. In this sense, Argentina’s early data protection framework combined institutional modernization with continuity in constitutional understandings of privacy and informational self-determination.

Argentina’s adequacy recognition by the European Commission in 2003 reinforced the international orientation of this governance model. Public discussions surrounding the law frequently associated data protection with democratic legitimacy, international integration, and participation in the expanding digital economy. The subsequent consolidation of oversight functions within the Agency for Access to Public Information (AAIP) further institutionalized a centralized rights-based approach to digital governance.

Argentina’s contemporary AI policy discourse differs notably from the more centralized and comprehensive logic that characterized its earlier data protection regime. Rather than constructing AI primarily as an autonomous regulatory domain requiring immediate institutional overhaul, official policy documents tend to incorporate AI within broader narratives of digital transformation, innovation, and public-sector modernization: the National Artificial Intelligence Plan (ArgenIA, 2019) repeatedly frames AI as an instrument for “desarrollo económico y social,” “innovación,” and technological modernization.

The ArgenIA Plan also reveals that AI is not primarily constructed as an isolated technological threat, but rather as part of a broader state-led modernization agenda linked to digital inclusion, productive transformation, and technological development. The document explicitly situates AI within the institutional horizon of the Agenda Digital Argentina 2030 and the national science and technology strategy Argentina Innovadora 2030. The Plan states that “[l]a adopción de IA es, sin dudas, una parte significativa del proceso de digitalización” and highlights the need to coordinate digital policies around “el desarrollo económico, la inclusión digital de todos los argentinos y la construcción de un gobierno eficiente y centrado en el ciudadano.” Rather than emphasizing existential technological rupture, the discourse repeatedly frames AI as an enabling instrument embedded within pre-existing priorities such as digital infrastructure, education, innovation, and state modernization. This orientation is reinforced when the Plan describes the Agenda Digital Argentina 2030 as a framework designed to “ordena[r] prioridades y darle impulso a iniciativas relacionadas con la promoción de marcos jurídicos que permitan aprovechar las oportunidades digitales.”

At the same time, the document also constructs AI through a developmental and geopolitical imaginary that combines opportunity, competitiveness, and international insertion: the Plan repeatedly associates AI with the strengthening of national scientific and productive capacities, arguing that the Estrategia Nacional de Ciencia, Tecnología e Innovación Productiva seeks to promote “una estructura productiva más compleja e intensiva en conocimiento,” alongside “un alto crecimiento sostenible y la inserción inteligente en el mundo.” The language of “future challenges,” “capacidades de frontera,” and “transformación de sectores de la economía” positions AI less as a narrowly legal problem than as a strategic technology tied to economic modernization and state capacity. Importantly, the Plan also legitimizes its orientation through international alignment, citing OECD AI Principles and UNESCO initiatives promoting “Inteligencia Artificial innovadora, confiable y de acuerdo a derechos humanos y valores democráticos.”

Similarly, Disposición 2/2023 on “Recomendaciones para una Inteligencia Artificial Fiable” describes AI as possessing “potencial transformador” capable of “promover la innovación, agregar valor, incrementar la productividad del trabajo” and improving public-sector governance through more efficient policy implementation and service provision. At the same time, it incorporates an increasingly visible ethical and rights-oriented discourse by explicitly emphasizing “crecimiento inclusivo,” “valores y equidad centrados en el ser humano,” and the obligation to respect “los derechos humanos y los valores democráticos durante todo el ciclo de vida del sistema de IA.” Risks associated with algorithmic bias, opacity, discrimination, and automated decision-making––as well as AI’s “potencial transformador”––are therefore acknowledged, but generally within a broader framing centered on responsible digital transformation rather than technological exceptionalism.

The Argentine corpus thus tends to construct AI less as a radically distinct governance challenge and more as an extension of ongoing debates concerning digital modernization, public innovation, and rights protection in data-intensive environments.

Argentina’s emerging AI governance architecture remains relatively dispersed and exploratory. Unlike Brazil’s attempt to consolidate a comprehensive statutory framework, Argentina has advanced through a combination of soft-law instruments, ethical guidelines, draft legislation, and institutional coordination initiatives. Recent congressional proposals between 2023 and 2024 include bills concerning algorithmic transparency, civil liability, human oversight in automated decision-making, AI observatories, and advisory councils. However, these initiatives remain fragmented across multiple institutional arenas and have not converged into a unified governance framework.

This distributed regulatory orientation is also reflected in the language of official guidance documents. Disposición 2/2023 repeatedly presents its recommendations as non-binding “herramientas teóricas y prácticas” intended to support public-sector actors engaged in AI-related innovation processes. The emphasis falls less on coercive enforcement and more on responsible adoption, institutional learning, and gradual adaptation of existing governance mechanisms.

At the same time, some legislative proposals increasingly incorporate stronger oversight-oriented language, particularly regarding high-risk AI systems, discriminatory outcomes, and automated decision-making in public administration. These initiatives illuminate a growing concern with accountability and institutional safeguards, even if governance discussions remain comparatively decentralized and normatively heterogeneous.

All in all, the Argentine regulatory debate reflects an important tension between innovation-oriented discourse and emerging demands for stronger institutional coordination. Existing digital governance repertoires continue to shape AI discussions, but the corpus also reveals increasing concern with the limitations of purely voluntary or principle-based approaches.

The Argentine case suggests a predominantly continuity-oriented approach to AI governance. Across the documents analyzed, AI is repeatedly incorporated into broader agendas concerning digital transformation, modernization, innovation policy, and responsible technological development rather than consistently framed as a radically autonomous regulatory domain.

Continuity is particularly visible in the persistence of rights-based language inherited from earlier data protection debates. Concerns regarding transparency, discrimination, accountability, and human oversight are frequently articulated through concepts already familiar within Argentina’s existing digital governance architecture. In this sense, the country’s earlier experience with data protection continues to provide an important interpretive repertoire for contemporary AI discussions.

At the same time, the corpus also reveals signs of gradual institutional differentiation. Legislative proposals concerning AI-specific oversight, algorithmic auditing, and civil liability indicate growing recognition that certain AI-related risks may require more specialized governance mechanisms. Nevertheless, these tendencies remain relatively fragmented and exploratory compared to more centralized regulatory trajectories elsewhere in the region.

Rather than reflecting either a fully consolidated governance model or a clear process of institutional rupture, the Argentine case illustrates how AI governance may emerge incrementally through dispersed policy experimentation, ethical guidance, and adaptation of pre-existing digital governance frameworks.

Brazil. Brazil’s contemporary AI governance debates emerge from a comparatively dense digital rights architecture consolidated during the 2010s. The Marco Civil da Internet (2014) and the Lei Geral de Proteção de Dados (LGPD) (2018) established an institutional and normative framework centered on privacy, accountability, transparency, and fundamental rights in digital environments. Rather than creating an entirely new legal logic, the LGPD consolidated previously fragmented sectoral protections into a broader governance framework influenced by the European GDPR while adapted to Brazilian constitutional traditions concerning privacy and habeas data (Erickson, 2018; Lorenzon, 2021).

The LGPD framed digital governance problems primarily in terms of informational asymmetries, misuse of personal data, discrimination, and lack of accountability in data processing activities. Particularly significant was the law’s emphasis on personal data (dado pessoal) and sensitive personal data (dado pessoal sensível), the latter including information related to “origem racial ou étnica, convicção religiosa, opinião política, filiação a sindicato ou a organização de caráter religioso, filosófico ou político, dado referente à saúde ou à vida sexual, dado genético ou biométrico.” The law also institutionalized preventive governance mechanisms through obligations related to transparency, security, risk management, and impact assessment procedures (“medidas, salvaguardas e mecanismos de mitigação de risco”).

At the institutional level, the creation of the National Data Protection Authority (ANPD) represented an important shift toward centralized administrative oversight. However, this innovation was largely articulated within an already existing digital rights framework rather than as a complete regulatory rupture. The LGPD therefore combined institutional modernization with continuity in Brazil’s broader trajectory of rights-oriented digital governance.

Brazil’s contemporary AI policy discourse builds extensively upon the governance architecture consolidated through the LGPD while concurrently presenting AI as a strategic technological frontier linked to national modernization, competitiveness, and international projection. The Estratégia Brasileira de Inteligência Artificial (EBIA), published in 2021, repeatedly frames AI through the vocabulary of economic development and innovation policy, emphasizing the need to “estimular a pesquisa, inovação e desenvolvimento de soluções em Inteligência Artificial” and to strengthen Brazil’s insertion into the global digital economy. Throughout the document, AI is associated with productivity gains, competitiveness, data-driven industrial transformation, and the creation of “ecossistemas e plataformas” capable of generating innovation through large-scale data use. The strategy explicitly situates Brazil within an international technological race, noting that “a corrida global por liderança em IA” requires proactive state coordination and international engagement. This developmental framing is reinforced through repeated references to strategic sectors where Brazil allegedly possesses “vantagem competitiva,” including agriculture, mining, finance, and health. AI therefore appears not only as a regulatory challenge but also as a geopolitical and economic opportunity tied to state capacity, international competitiveness, and long-term national development.

At the same time, the EBIA incorporates a parallel discourse centered on ethics, accountability, transparency, and human rights protection, producing a more internally heterogeneous policy narrative than a purely innovation-oriented reading would suggest. The strategy repeatedly insists that AI systems “não devem criar ou reforçar preconceitos capazes de impactar de maneira injusta ou desproporcional determinados indivíduos,” explicitly referencing race, gender, disability, religion, sexuality, and political orientation as protected categories. The document further argues that concerns with “dignidade humana” and “bem-estar humano” must be present “desde a concepção (ethics by design)” of AI systems. Rather than advocating immediate hard-law intervention, however, the EBIA frequently emphasizes regulatory caution and proportionality. It warns against regulations that could “desnecessariamente limitar a inovação, a adoção e o desenvolvimento de IA” and recommends balancing legal intervention with the preservation of innovation ecosystems. This tension becomes particularly visible in the governance section, where the strategy advocates risk assessments, algorithmic explainability, impact reports, ethics committees, accountability structures, and “sandboxes regulatórios,” while also insisting that AI governance should remain technologically neutral and avoid regulatory duplication with the LGPD. The resulting discourse does not present a single coherent regulatory philosophy. Instead, it combines developmental ambition, institutional caution, human-rights language, and innovation-oriented flexibility within the same policy framework.

These tensions become more visible in legislative debates surrounding Bill 2338/2023, Brazil’s proposed comprehensive AI framework. The bill adopts a risk-based regulatory architecture strongly oriented toward the protection of fundamental rights, transparency obligations, accountability, and human oversight. The proposal repeatedly emphasizes “riscos sistêmicos”, “risco excessivo”, “impacto algorítmico” as well as its corresponding “prevenção, precaução e mitigação”; “participação humana no ciclo da inteligência artificial”, “supervisão humana efetiva,” “explicabilidade”, “inteligibilidade”, “auditabilidade,” and “não-discriminação” –among others– as central governance principles.

The proposal also introduces governance mechanisms that partially extend the logic of Brazil’s data protection regime into the AI domain. Rights to information, explanation, contestation, and human review (“direito de contestar decisões e de solicitar intervenção humana”) echo protections already consolidated under the LGPD (see laws 13.709 and 13.853). Similarly, the proposed oversight structure preserves a strong role for administrative supervision and regulatory coordination.

However, the bill constructs AI as a sufficiently distinct technological domain to justify specialized governance mechanisms and autonomous regulatory treatment. In this respect, the creation of differentiated risk categories, prohibitions on “sistemas de inteligência artificial de risco excessivo,” algorithmic impact assessments, and AI-specific supervisory arrangements are particularly illustrative examples of a movement beyond incremental adaptation alone. In this sense, Brazil’s regulatory debate combines continuity with existing digital governance repertoires and increasing tendencies toward AI exceptionalization.

The Brazilian corpus depicts a predominantly continuity-oriented regulatory trajectory, insofar as AI governance is repeatedly articulated through legal principles, institutional arrangements, and rights-based frameworks previously consolidated in data protection governance. The LGPD functions less as a direct causal determinant than as an important institutional and normative reference point through which AI-related risks become intelligible within public policy debates.

At the same time, Brazilian AI governance discussions also reveal important novelty-oriented tendencies––specifically encompassed in the vocabulary introduced. Policy and legislative documents increasingly portray AI as a strategic and transformative technological domain requiring dedicated governance instruments, specialized oversight mechanisms, and differentiated regulatory categories. Rather than reflecting a purely incremental model, the Brazilian case illustrates how inherited digital governance frameworks may coexist with emerging forms of AI-specific institutional innovation.

Chile. Chile’s digital governance trajectory initially developed through a predominantly privacy-centered legal framework grounded in constitutional protections of private life. Law No. 19.628 on the Protection of Personal Data and Private Life (Protección de la Vida Privada y Datos Personales) (1999) constituted the country’s first major statutory response to the expansion of digital databases and large-scale personal information processing. The law established rules concerning consent, access, rectification, and judicial remedies related to personal data processing, particularly in contexts involving financial records, commercial databases, and public registries. In this earlier regulatory framework, technological novelty was addressed primarily through continuity with existing constitutional and judicial traditions. Rather than constructing digital governance as an autonomous regulatory domain, the law treated emerging data-processing challenges as an extension of broader concerns surrounding privacy and informational abuse. Enforcement mechanisms remained largely reactive and court-centered, with limited administrative oversight capacity.

This orientation began to shift significantly with Law No. 21.719, which reforms Chile’s data protection regime and creates a dedicated Personal Data Protection Agency. The reform introduces concepts such as “perfilamiento,” “datos personales sensibles,” “anonimización,” and “seudonominización,” among others, while incorporating preventive governance mechanisms associated with contemporary digital regulation, including Data Protection Impact Assessments (“Evaluación de impacto en protección de datos personales”) (DPIAs) for high-risk processing activities. Public legal analyses surrounding the reform describe the new framework as moving beyond the traditional language of “vida privada” toward a comprehensive definition of personal data aligned with international standards such as the GDPR.

The reform also expands the role of accountability, transparency, and administrative supervision within Chilean digital governance. Discussions surrounding the implementation of Law No. 21.719 repeatedly emphasize obligations related to “[e]valuación sistemática y exhaustiva” and “protección desde el diseño y por defecto,” particularly in contexts involving large-scale or automated processing. Taken together, these developments illustrate an important institutional transition from a predominantly remedial model toward a more preventive and administratively coordinated––and, so to speak, modern––approach to data governance.

Chile’s AI policy discourse constructs artificial intelligence simultaneously as a strategic opportunity for national development and as a source of emerging governance risks requiring anticipatory institutional responses. The Política Nacional de Inteligencia Artificial (2021) repeatedly frames AI through the language of modernization, productivity, sustainability, and international competitiveness, presenting it as a key technology for the country’s future economic and institutional development. The document explicitly states that “la Inteligencia Artificial es una tecnología estratégica para el desarrollo sostenible y el crecimiento de Chile,” while also emphasizing its capacity to “crear nuevas oportunidades de innovación, emprendimiento y desarrollo económico y social.” AI is associated throughout the policy with improvements in public administration, scientific development, industrial competitiveness, healthcare, education, and environmental management. The strategy further argues that AI can contribute to “nuevas capacidades en los sectores productivos, académicos y estatales,” strengthening Chile ‘s insertion into global technological transformations. This developmental orientation is reinforced through repeated references to infrastructure investment, digital connectivity, research ecosystems, and public-private coordination. The policy proposes “fomentar inversión privada, pública y público-privada en infraestructura tecnológica”, and explicitly links AI development to Chile ‘s “ventajas comparativas a nivel global.” In this sense, AI is not framed merely as a regulatory problem but as a strategic field connected to state modernization, economic diversification, and technological sovereignty.

At the same time, the policy consistently incorporates ethical and rights-based concerns into this developmental narrative, producing a more layered and internally heterogeneous discourse. The National AI Policy explicitly identifies “una IA con centro en el bienestar de las personas, el respeto a los derechos humanos y la seguridad” as one of its core principles, emphasizing that technological deployment must remain aligned with democratic values and public oversight. The document repeatedly references risks associated with algorithmic discrimination, opacity, concentration of technological power, exclusion, and labor transformation, implying that AI-related challenges are understood not only in technical terms but also as institutional and social problems. In particular, the policy warns that AI systems may “replicar o amplificar sesgos existentes” if governance mechanisms are insufficiently developed. The strategy therefore advocates anticipatory governance tools centered on transparency, accountability, inclusion, and multi-stakeholder coordination. It calls for strengthening regulatory capacities, modernizing oversight institutions, and ensuring that public agencies possess “las competencias necesarias de fiscalización” in relation to emerging digital infrastructures and technologies.

The 2024 Chilean bill regulating artificial intelligence systems (Proyecto de Ley 16821-19) substantially reinforces the hybrid regulatory trajectory identified in Chile’s broader AI governance framework. On the one hand, the project constructs AI as a strategic and potentially beneficial technology whose development should be actively promoted by the state. Article 1 explicitly states that the law seeks to “promover la creación, desarrollo, innovación e implementación de sistemas de inteligencia artificial (“IA”) al servicio del ser humano,” linking AI to innovation and technological development rather than treating it exclusively as a source of risk. The bill repeatedly incorporates innovation-oriented governance instruments, including “espacios controlados de pruebas” (regulatory sandboxes) designed to facilitate experimentation and validation of AI systems before market deployment. At the same time, however, the project adopts a highly structured risk-based regulatory architecture strongly influenced by the European AI Act. The text introduces detailed classifications of “riesgo inaceptable,” “alto riesgo,” and “riesgo limitado,” while defining AI-related harms through categories such as biometric surveillance, emotion recognition, profiling, and threats to “derechos fundamentales.” This combination shows that Chilean policymakers increasingly frame AI not merely as an extension of existing digital governance challenges, but as a technological field requiring specialized conceptual categories, anticipatory oversight, and differentiated regulatory intervention.

At the same time, the bill does not fully abandon continuity with Chile’s existing rights-based and data-protection-oriented governance tradition. Rather than constructing AI governance as institutionally autonomous, the project repeatedly embeds AI oversight within broader frameworks of privacy, consumer protection, democratic accountability, and administrative supervision. Article 4 establishes principles such as “intervención y supervisión humana,” “privacidad y gobernanza de datos,” “transparencia y explicabilidad,” and “diversidad, no discriminación y equidad,” explicitly linking AI governance to pre-existing constitutional and regulatory concerns. The bill further states that AI systems must operate “al servicio del ser humano, que respete la dignidad humana y la autonomía personal,” while also requiring conformity with existing data-protection norms and consumer-rights legislation. Institutionally, the project does not seek to create a wholly autonomous AI authority; instead, it distributes governance responsibilities across the Ministry of Science, the––future––Data Protection Agency, and existing administrative structures.

The Chilean policy corpus therefore reflects a dual interpretive movement. On the one hand, AI is described as a transformative technological domain associated with economic modernization, scientific development, and public-sector innovation. On the other hand, policy documents repeatedly frame AI as a technology capable of intensifying existing risks related to discrimination, opacity, asymmetrical power relations, and vulnerabilities affecting children and other socially exposed groups. Rather than treating these concerns as mutually exclusive, Chilean policy discourse tends to articulate innovation and rights protection simultaneously within a shared governance framework.

These overlapping interpretive tendencies become more visible when comparing Chile’s broader AI policy framework with legislative proposals concerning AI-related criminal harms. Several congressional initiatives introduced between 2023 and 2024 seek to modify the Penal Code in response to AI-enabled fraud, identity manipulation, sexual image fabrication, and deceptive synthetic media practices. In these debates, AI is frequently constructed as an accelerant of existing criminal risks capable of outpacing current legal frameworks. Legislative discussions surrounding these proposals often emphasize urgency and regulatory adaptation. Public justifications for reform repeatedly invoke the need to avoid “quedarse atrás” in the face of rapidly evolving technological capabilities and emerging forms of digital harm. Within this penal framing, AI appears less as an ordinary extension of existing digital governance problems and more as a novel threat requiring targeted legal intervention and updated criminal categories.

At the same time, Chile’s broader governance architecture approaches AI through a more preventive and institutionalized regulatory logic. As mentioned above, the National AI Policy and the reformed data protection framework increasingly emphasize administrative oversight, risk management, ethical governance, impact assessments, and sectoral coordination. Regulatory instruments associated with Law No. 21.719, including DPIAs and safeguards concerning automated processing and profiling, partially incorporate algorithmic governance concerns into an expanded data protection regime.

This coexistence of penal exceptionalism and administrative normalization produces a layered regulatory orientation. Certain AI applications—particularly those associated with fraud, deepfakes, or sexual exploitation—are treated through urgent and punitive regulatory responses. Meanwhile, broader governance discussions frame AI as a manageable socio-technical system requiring institutional coordination, ethical safeguards, and long-term regulatory adaptation.

The Chilean case suggests one of the clearest examples of hybrid governance within the comparative corpus analyzed in this article. Rather than consistently framing AI either as continuity or rupture, Chilean regulatory and policy documents combine multiple interpretive logics that coexist across different institutional arenas.

Continuity-oriented tendencies remain visible in the persistent emphasis on privacy, human rights, transparency, and administrative accountability inherited from earlier digital governance debates. The reformed data protection framework increasingly incorporates AI-related concerns into existing regulatory repertoires concerning profiling, risk management, and automated processing. In this sense, AI governance is partially normalized through expansion and adaptation of pre-existing institutional mechanisms.

All together, novelty-oriented tendencies emerge strongly in criminal law proposals and policy discussions emphasizing technological acceleration, legal insufficiency, and the need for anticipatory governance instruments. Certain AI-enabled harms are portrayed as sufficiently distinct to justify specialized legal responses and updated institutional capacities.

The Chilean corpus reflects neither a purely incremental nor a fully exceptionalist approach to AI governance. Instead, the documents analyzed here configure a hybrid regulatory orientation in which punitive responses to acute harms coexist with broader attempts to integrate AI governance into preventive, rights-based, and administratively coordinated regulatory frameworks. This combination makes Chile particularly valuable for comparative analysis, since it illustrates how multiple regulatory narratives may coexist simultaneously within emerging AI governance regimes.

Mexico. Mexico’s contemporary digital governance framework was consolidated through the Federal Law on the Protection of Personal Data Held by Private Parties (Ley Federal de Protección de Datos Personales en Posesión de los Particulares) (LFPDPPP), enacted in 2010. The law represented the country’s first comprehensive regulatory response to large-scale personal data processing associated with internet expansion, digital platforms, and cross-border information flows. It established a rights-based governance framework centered on consent, transparency, accountability, and informational self-determination, while institutionalizing the ARCO rights of “acceso, rectificación, cancelación u oposición”. Mexico’s data protection framework also associated privacy governance with broader participation in the digital economy. Public and legal discussions surrounding the LFPDPPP frequently presented personal data protection as a necessary condition for trust, technological modernization, and international economic integration.

The law framed digital governance problems primarily through concerns regarding unlawful or opaque processing of personal information, particularly sensitive data capable of generating discrimination or harm. Regulatory language repeatedly emphasized the need to protect individuals against unauthorized use of personal data and asymmetrical informational relations between organizations and data subjects. The creation of a specialized supervisory authority—today the National Institute for Transparency, Access to Information and Personal Data Protection (Instituto Nacional de Transparencia, Acceso a la Información y Protección de Datos Personales) (INAI)^[2]—further reinforced a centralized administrative approach to data governance.

Unlike several other Latin American countries, by 2024 Mexico has not adopted a unified national AI strategy. Instead, AI-related debates have emerged through a dispersed set of legislative initiatives addressing specific harms and sectoral concerns. Across these proposals, AI is frequently constructed less as an autonomous technological domain and more as a mechanism capable of intensifying pre-existing social risks.

This framing is particularly visible in legislative discussions concerning digital gender violence, deepfakes, identity theft, cyber harassment, and fraudulent synthetic content. Congressional initiatives repeatedly describe AI-generated content as threatening “la dignidad,” “la intimidad,” and the protection of women, children, and vulnerable populations. Public debates surrounding these proposals frequently emphasize the harmful consequences of “contenidos íntimos sexuales falsos” and AI-enabled impersonation practices through deepfakes.

Within this corpus, AI is commonly portrayed as an accelerant of existing forms of violence and deception rather than exclusively as a driver of innovation or economic transformation. Risks associated with misinformation, manipulation, cybercrime, and synthetic media receive substantially greater attention than themes such as competitiveness or technological sovereignty. At the same time, some policy discussions—particularly in healthcare and public administration—acknowledge potential benefits related to diagnostics, efficiency, and service modernization, although these opportunity-oriented narratives generally remain secondary within the broader regulatory debate. The Mexican corpus therefore tends to construct AI primarily through a harm-mitigation lens centered on protection against intensified forms of social, digital, and gender-based vulnerability.

Mexico’s regulatory orientation toward AI remains strongly sectoral and adaptive. Rather than proposing a centralized AI governance authority or a comprehensive statutory framework, most initiatives seek to incorporate AI-related concerns into already existing legal and institutional structures. Legislative reforms focus particularly on criminal law, gender violence legislation, public security, copyright, labor rights, and healthcare governance. This orientation is especially visible in proposals addressing deepfakes and AI-generated sexual content. Several initiatives seek to modify the Federal Penal Code and the General Law on Women’s Access to a Life Free of Violence by introducing aggravating circumstances, new criminal categories, or obligations concerning removal and blocking of harmful synthetic content. Such bills emphasize the rapid growth of AI-enabled abuse and the need to adapt existing legal instruments to emerging technological practices.

In parallel, sector-specific governance proposals increasingly reference ethical principles such as transparency, accountability, informed consent, and privacy-by-design, particularly in healthcare-related discussions. However, these initiatives remain institutionally dispersed across existing agencies and regulatory bodies rather than converging into a unified governance architecture.

This fragmented regulatory orientation produces both flexibility and institutional dispersion. Existing governance mechanisms can be adapted relatively quickly to emerging harms, but the absence of a centralized AI framework also limits the development of consistent cross-sectoral regulatory standards.

The Mexican case enshrines a predominantly continuity-oriented approach to AI governance centered on risk mitigation and sectoral adaptation. Across the documents analyzed, AI is rarely constructed as requiring an entirely autonomous governance regime. Instead, most initiatives seek to integrate AI-related risks into already existing legal repertoires concerning criminal law, digital violence, consumer protection, health regulation, and human rights.

Continuity is particularly visible in the reliance on pre-existing institutions such as INAI, criminal courts, healthcare authorities, and sectoral regulatory agencies. AI governance discussions repeatedly frame technological risks through familiar categories including fraud, harassment, privacy violations, discrimination, and violence against women.

At the same time, the corpus also reveals emerging novelty-oriented tendencies. Certain legislative debates portray synthetic media, deepfakes, and AI-enabled impersonation as qualitatively new forms of harm capable of overwhelming traditional legal categories and enforcement capacities. This language occasionally introduces exceptionalist elements into the regulatory debate, particularly in contexts involving digital sexual violence and automated deception.

Nonetheless, the Mexican corpus remains characterized less by comprehensive institutional innovation than by fragmented and targeted adaptation of existing governance frameworks. The case illustrates how AI governance may emerge through the gradual extension of sectoral regulatory repertoires rather than through centralized technological governance models.

DISCUSSION: FRAMING, CONTINUITY, AND REGULATORY TIMING IN LATIN AMERICAN AI GOVERNANCE

Across the cases analyzed, the analytical dimensions outlined in Table 1 rarely appeared in isolated or internally coherent form. References to innovation, technological disruption, and institutional novelty frequently coexisted with continuity-oriented concerns regarding privacy, administrative accountability, discrimination, consumer protection, or existing constitutional principles. Rather than producing stable governance “models,” these dimensions operated as overlapping interpretive repertoires that policymakers mobilized differently according to institutional context, political priorities, and perceived risks.

This ambiguity was particularly visible in countries such as Chile and Brazil, where documents simultaneously emphasized anticipatory governance and institutional continuity, but it also appeared in more fragmented regulatory environments such as Argentina and Mexico. The comparative analysis therefore suggests that AI governance debates in Latin America are characterized less by clear regulatory paradigms than by negotiated combinations of inherited legal vocabularies and emerging narratives of technological transformation.

At the same time, important variations appear in how these continuities are articulated. Brazil represents the clearest attempt to consolidate AI governance within a centralized and relatively coherent institutional architecture. The trajectory from the LGPD toward Bill 2338/2023 hints a tendency to extend already consolidated data protection and digital rights repertoires into the AI domain. Yet even in Brazil, the regulatory discourse does not rely exclusively on continuity. Policy documents and legislative proposals describe AI both as a strategic opportunity for innovation and as a source of novel risks requiring anticipatory governance mechanisms, including specialized oversight structures and differentiated liability regimes.

Argentina presents a different configuration. Rather than moving toward comprehensive institutional consolidation, the Argentine corpus tends to incorporate AI within broader narratives of digital modernization, public innovation, and responsible technological development. Governance proposals remain relatively fragmented across soft-law instruments, ethical recommendations, draft legislation, and institutional initiatives. In this context, continuity operates less through centralized expansion than through incremental adaptation of existing digital governance vocabularies, particularly those associated with transparency, accountability, and rights protection. The documents analyzed point towards an exploratory and distributed regulatory orientation rather than a clearly stabilized governance model.

Chile occupies a more explicitly hybrid position. The corpus combines preventive, rights-based, and administrative approaches with more exceptionalist responses associated with criminal law reform and urgent protection against AI-enabled harms. The National AI Policy and the reform of Law No. 19.628 increasingly integrate algorithmic governance into a broader architecture of accountability, impact assessment, and administrative oversight. Simultaneously, penal initiatives addressing deepfakes, identity manipulation, and synthetic sexual content frame AI as a rapidly evolving threat capable of overwhelming existing legal categories. Rather than resolving these tensions, the Chilean case illustrates how multiple regulatory logics may coexist within the same governance environment.

Mexico, meanwhile, reveals a predominantly sectoral and harm-centered approach to AI governance. Across the initiatives analyzed, AI is frequently constructed less as a strategic technological domain than as a mechanism capable of intensifying pre-existing forms of violence, fraud, harassment, and social vulnerability. Regulatory responses therefore tend to emerge through adaptation of criminal law, gender violence legislation, health regulation, and consumer protection frameworks. This produces a fragmented but relatively flexible governance pattern in which AI-related risks are incorporated into already existing institutional and legal repertoires.

These cases suggest that the distinction between “novelty” and “continuity” operates less as a rigid typology than as a spectrum of interpretive tendencies that coexist and overlap across regulatory debates. Countries rarely frame AI exclusively as either rupture or extension. Instead, policy documents frequently combine narratives of technological transformation with references to familiar institutional concerns such as privacy, discrimination, administrative accountability, fraud, or human rights protection. In this sense, the governance of AI in Latin America appears characterized not by pure institutional innovation but by layered processes of reinterpretation and recombination.

This finding also complicates some assumptions frequently present in global AI governance debates. Much of the international literature, particularly in Europe and North America, tends to analyze AI regulation through discussions of comprehensive frameworks, existential risks, or highly centralized governance architectures (Brown et al., 2025; Veale et al., 2023). By contrast, the Latin American corpus examined here indicates that regulatory debates often emerge through more dispersed institutional trajectories shaped by pre-existing governance capacities, legal traditions, and policy priorities. AI governance is therefore frequently embedded within broader struggles concerning administrative coordination, digital inequality, violence, transparency, and institutional legitimacy.

The analysis additionally highlights the importance of regulatory timing and uncertainty in contemporary AI policymaking. Across multiple cases, documents repeatedly express concern regarding the possibility of “arriving late” to technological transformation, particularly in contexts involving synthetic media, automated decision-making, or platform-mediated harms. This temporal dimension introduces an important tension within AI governance debates. Framing AI as a radically novel phenomenon may generate pressure for rapid institutional innovation and anticipatory regulation. However, accelerated regulatory intervention also occurs under conditions of substantial technological uncertainty, limited enforcement capacity, and rapidly evolving technical architectures.

This tension resembles what the literature on responsible innovation and technological policy has described as the Collingridge dilemma (Genus & Stirling, 2018): regulating too early may produce rigid or obsolete frameworks, while regulating too late may allow harmful systems and institutional asymmetries to consolidate. Although the countries analyzed do not explicitly reference this literature, many of the debates examined implicitly reflect these concerns. Brazil’s attempt to develop a comprehensive framework, Chile’s coexistence of administrative and penal approaches, Argentina’s reliance on exploratory governance instruments, and Mexico’s sectoral adaptation strategies can all be interpreted partly as different responses to uncertainty regarding the speed, scale, and social consequences of AI diffusion.

The temporal dimension is particularly relevant in emerging economies, where institutional capacities, technological dependency, and resource constraints shape regulatory possibilities. Several documents analyzed combine aspirations toward international alignment with concerns regarding feasibility, implementation, and economic competitiveness. As a result, AI governance discussions frequently oscillate between anticipatory ambition and pragmatic adaptation. Rather than simply copying external regulatory models, the cases examined suggest ongoing attempts to domesticate global AI governance vocabularies within locally situated institutional contexts.

More broadly, the findings reinforce the importance of interpretive approaches for understanding technology governance. The comparative evidence illustrates that regulation is not only shaped by technological characteristics or policy instruments, but also by the narratives through which policymakers define urgency, risk, legitimacy, and institutional responsibility. Whether AI is framed as a continuation of existing governance challenges or as a fundamentally disruptive phenomenon affects which actors become central, which harms become visible, and which regulatory solutions appear politically and institutionally feasible.

At the same time, the analysis also indicates the limits of overly deterministic interpretations. The cases examined do not reveal stable or linear trajectories. Regulatory discourses remain fluid, internally contested, and subject to ongoing institutional negotiation. Many of the initiatives analyzed are recent, incomplete, or still under legislative discussion. Consequently, the patterns identified here should be interpreted less as definitive models of AI governance than as emerging configurations within a rapidly evolving regulatory field.

CONCLUSION

This article examined how policymakers in Argentina, Brazil, Chile, and Mexico frame artificial intelligence within contemporary regulatory debates, and how these framings shape emerging governance trajectories. Rather than treating AI regulation as a purely technical or legal process, the analysis approached governance as an interpretive and institutional phenomenon structured through narratives of novelty, continuity, risk, and opportunity. By comparing historical data protection frameworks with recent AI-related initiatives, the article explored how earlier experiences in digital governance continue to influence contemporary regulatory imaginaries in Latin America.

The comparative analysis hint that AI governance in the region rarely emerges through complete institutional rupture. Across the cases examined, policymakers frequently incorporate AI into already existing vocabularies associated with privacy, discrimination, administrative accountability, consumer protection, digital modernization, and human rights. At the same time, the documents analyzed also reveal recurrent attempts to construct AI as a technologically distinctive phenomenon requiring anticipatory intervention, specialized oversight, or new legal categories. Rather than constituting mutually exclusive positions, continuity and novelty often coexist within the same regulatory debates.

The cases examined illustrate different configurations of this tension. Brazil moves most clearly toward institutional consolidation and comprehensive governance frameworks grounded in pre-existing digital rights architectures. Argentina adopts a more exploratory and fragmented approach, relying heavily on soft-law instruments, ethical principles, and dispersed legislative experimentation. Chile combines preventive administrative governance with more exceptionalist penal responses to AI-enabled harms, producing a layered and hybrid regulatory configuration. Mexico, meanwhile, addresses AI primarily through sectoral adaptation and harm mitigation, particularly in criminal law and gender violence frameworks. These differences imply that AI governance trajectories are shaped not only by global technological narratives but also by local institutional capacities, political priorities, and regulatory traditions.

More broadly, the findings indicate that AI governance debates in Latin America are characterized by interpretive flexibility rather than stable regulatory models. The same technology may simultaneously be framed as an economic opportunity, a source of administrative modernization, an amplifier of structural inequalities, or a threat requiring urgent intervention. As a result, governance strategies frequently combine overlapping and sometimes internally contradictory logics. The article therefore argues against rigid distinctions between “innovation-oriented” and “rights-based” governance or between “continuity” and “rupture.” In practice, regulatory responses tend to emerge through gradual processes of institutional adaptation, layering, and reinterpretation.

The analysis also highlights the importance of temporality and uncertainty in contemporary AI governance. Across the corpus examined, policymakers repeatedly express concern regarding the speed of technological change and the possibility of institutional lag. Yet efforts to regulate AI occur under conditions of incomplete knowledge regarding the future social, economic, and political consequences of these systems. This tension reflects a broader challenge identified in the literature on emerging technologies: regulating too early may generate rigid or quickly obsolete frameworks, while regulating too late may allow harmful practices and asymmetries to consolidate. The cases analyzed can therefore be understood partly as different attempts to navigate uncertainty through combinations of precaution, flexibility, incrementalism, and institutional experimentation.

This article contributes to interpretive approaches in technology governance by emphasizing the importance of problem framing in shaping regulatory trajectories. The analysis brings to mind that governance outcomes cannot be understood exclusively through institutional design or legal instruments. Regulatory debates are also structured by narratives that define what AI is, which harms become visible, which actors are considered responsible, and which forms of intervention appear politically legitimate. Examining these interpretive dimensions is particularly relevant in emerging economies, where AI governance debates intersect with broader concerns regarding development, inequality, institutional capacity, and digital dependency.

At the same time, the article has several limitations. First, the analysis focuses on official documents and legislative initiatives rather than implementation or enforcement practices. Consequently, the study examines how AI governance is articulated discursively rather than how regulation operates in practice. Second, the corpus analyzed is necessarily partial and temporally bounded within a rapidly evolving policy field. Many of the initiatives discussed remain under legislative debate or institutional development. Third, the comparative design prioritizes interpretive depth over regional representativeness; the findings therefore should not be generalized to Latin America as a whole.

Future research could build on this analysis by examining how AI governance frameworks are implemented administratively, contested politically, or adapted judicially over time. Comparative work involving additional countries, sectoral case studies, or interviews with policymakers and civil society actors would also help clarify how interpretive framings translate into concrete governance practices. Similarly, further research might explore how global AI governance models are selectively appropriated, resisted, or transformed within different institutional contexts across the Global South.

Ultimately, the cases examined here point out that AI governance in Latin America is developing less through wholesale institutional reinvention than through ongoing processes of reinterpretation and regulatory adaptation. Even in debates surrounding emerging technologies, policymakers continue to rely on inherited legal categories, institutional repertoires, and familiar narratives to render technological change intelligible and governable. In that sense, the governance of AI appears shaped not only by technological novelty itself, but also by the institutional memories through which novelty is interpreted.

REFERENCES

1. Aguerre, C., & Galperin, H. (2015, November). Internet Policy Formation in Latin America: Understanding the links between the National, the regional, and the Global. In GigaNet: Global Internet Governance Academic Network, Annual Symposium.
2. Bacchi, C. (2009). Analysing policy. Pearson Higher Education AU.
3. Boczkowski, P. J. (2005). Digitizing the news: Innovation in online newspapers. MIT Press.
4. Brown, V., Larasati, R., Kwarteng, J., & Farrell, T. (2025). Understanding AI and power: situated perspectives from Global North and South practitioners. AI & SOCIETY, 1-16.
5. Capoccia, G., & Kelemen, R.D. (2007). The study of critical junctures: Theory, narrative, and counterfactuals in historical institutionalism. World politics, 59 (3), 341-369.
6. Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376 (2133), 20180080.
7. Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
8. Crespi, G., & Dutrénit, G. (2014). Introduction to science, technology and innovation policies for development: The Latin American experience. In Science, Technology and Innovation Policies for Development: The Latin American Experience (pp. 1-14). Cham: Springer International Publishing.
9. Del Campo, A. (2025). Digital Constitutionalism in Latin America. CELE Research Paper, (65).
10. Del Campo, A., Zara, N., & Álvarez-Ugarte, R. (2025). Reclaiming Human Rights for Platform Governance: Proposals for Restoring Their Centrality in the Era of Risks. CELE Research Paper, (66).
11. Douglas, M. (1986). How institutions think . Syracuse University Press.
12. Elish, M.C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI. Communication monographs , 85 (1), 57-80.
13. Erickson, A. (2018). Comparative Analysis of the EU’s GDPR and Brazil’s LGPD: Enforcement Challenges with the LGPD. Brook. J. Int’l L., 44, 859.
14. Fischer, F. (2003). Reframing public policy: Discursive politics and deliberative practices. Oxford University Press.
15. Floridi, L. (2020). Artificial intelligence as a public service: Learning from Amsterdam and Helsinki. Philosophy & Technology , 33 (4), 541-546.
16. Galperin, H. (2004). New television, old politics: the transition to digital TV in the United States and Britain. Cambridge University Press.
17. Genus, A., & Stirling, A. (2018). Collingridge and the dilemma of control: Towards responsible and accountable innovation. Research policy, 47(1), 61-69.
18. Gil-Garcia, J. R., Helbig, N., & Ojo, A. (2014). Being smart: Emerging technologies and innovation in the public sector. Government information quarterly , 31 , I1-I8.
19. Gurumurthy, A., & Chami, N. (2017). A feminist action framework on development and digital technologies.
20. Hajer, M.A. (1995). The politics of environmental discourse: Ecological modernization and the policy process. Clarendon Press.
21. Hård, M., & Jamison, A. (2013). Hubris and hybrids: A cultural history of technology and science . Routledge.
22. Hilbert, M. (2020). Digital technology and social change: the digital transformation of society from a historical perspective. Dialogues in clinical neuroscience , 22 (2), 189-194.
23. Jasanoff, S., & Kim, S.H. (Eds.). (2019). Dreamscapes of modernity: Sociotechnical imaginaries and the fabrication of power . University of Chicago Press.
24. Kingdon, J. W. (1995). Agendas, alternatives, and public policies. 2nd. NY: HarperCollins College Publisher.
25. Kop, M. (2021, September). EU Artificial Intelligence Act: The European approach to AI. Stanford-Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue.
26. Lodge, M., & Wegrich, K. (2012). Managing regulation: Regulatory analysis, politics and policy . Bloomsbury Publishing.
27. Lorenzon, L. N. (2021). Análise comparada entre regulamentações de dados pessoais no Brasil e na União Europeia (LGPD e GDPR) e seus respectivos instrumentos de enforcement. Revista do Programa de Direito da União Europeia, 1, 39-52.
28. Maas, M. M. (2025). Architectures of Global AI Governance: From Technological Change to Human Choice. Oxford University Press.
29. Mahoney, J. (2000). Path dependence in historical sociology. Theory and society, 29 (4), 507-548.
30. Margetts, H., & Dunleavy, P. (2013). The second wave of digital-era governance: a quasi-paradigm for government on the Web. Philosophical transactions of the royal society A: mathematical, physical and engineering sciences , 371 (1987), 20120382.
31. Marvin, C. (1988). When old technologies were new: Thinking about electric communication in the late nineteenth century. Oxford University Press.
32. Milan, S., & Treré, E. (2019). Big data from the South(s): Beyond data universalism. Television & New Media, 20 (4), 319-335.
33. Oldenburg, N., & Papyshev, G. (2025). The Stories We Govern By: AI, Risk, and the Power of Imaginaries. arXiv preprint arXiv:2508.11729.
34. Pierson, P. (2000). Increasing returns, path dependence, and the study of politics. American political science review, 94 (2), 251-267.
35. Pretel, D., Inkster, I., & Wendt, H. (2019). Technology in Latin American History: Perspectives, Scales and Comparisons. History of Technology, 34 , 1-21.
36. Rein, M. and Schön, D. A. (1993) Reframing policy discourse. In Fischer, F. and Forester, J. (eds.) The argumentative turn in policy, analysis and planning, Durham: Duke University Press, 145–166.
37. Rochefort, DA, & Cobb, RW (Eds.). (1994). The politics of problem definition: Shaping the policy agenda. University Press of Kansas.
38. Shine, Z. W. and Bregman, D. M. 2025. “US Administration Rolls Out America’s AI Action Plan.” Morgan Lewis, July 25, 2025. https://www.morganlewis.com/pubs/2025/07/us-administration-rolls-out-americas-ai-action-plan 
39. Stone, D.A. (1989). Causal stories and the formation of policy agendas. Political science quarterly, 104 (2), 281-300.
40. Veale, M., & Edwards, L. (2018). Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Computer Law & Security Review, 34 (2), 398-404.
41. Veale, M., Matus, K., & Gorwa, R. (2023). AI and global governance: Modalities, rationales, tensions. Annual Review of Law and Social Science, 19(1), 255-275.
42. Winston, B. (2002). Media, technology and society: A history: From the telegraph to the Internet. Routledge.

 

Official documents

Argentina

Argentina. Decreto No. 1160/2010 Modificatorio del Decreto Reglamentario de la Ley No. 25.326 de Protección de Datos Personales. Boletín Oficial de la República Argentina, 2010.

Argentina. Decreto No. 1558/2001 Reglamentario de la Ley No. 25.326 de Protección de Datos Personales. Boletín Oficial de la República Argentina, 2001.

Argentina. Disposición 2/2023. Recomendaciones para una Inteligencia Artificial Fiable en el Sector Público. Boletín Oficial de la República Argentina, 2023.

Argentina. Ley No. 25.326 de Protección de los Datos Personales. Boletín Oficial de la República Argentina, 2000.

Argentina. Plan Nacional de Inteligencia Artificial (ArgenIA). Presidencia de la Nación, Argentina, 2019.

Argentina. Proyecto de Ley 1472-D-2023. Modificaciones sobre Inteligencia Artificial en la Ley de Ciencia, Tecnología e Innovación (Ley 25.467). Cámara de Diputados de la Nación, 2023.

Argentina. Proyecto de Ley 1747/2023. Control del Desarrollo, Implementación y Utilización de Sistemas Basados en Inteligencia Artificial. Cámara de Diputados de la Nación, 2023.

Argentina. Proyecto de Ley 3161-D-2023. Creación del Consejo Federal de Inteligencia Artificial. Cámara de Diputados de la Nación, 2023.

Argentina. Proyecto de Ley 4329-D-2023. Regulación y Uso de la Inteligencia Artificial. Cámara de Diputados de la Nación, 2023.

Argentina. Proyecto de Ley Marco Legal para la Investigación, Desarrollo, Uso y Regulación de la Inteligencia Artificial. Cámara de Diputados de la Nación, 2024.

Argentina. Proyecto de Ley de Creación del Observatorio Federal sobre Inteligencia Artificial. Cámara de Diputados de la Nación, 2024.

 

Brazil

Brazil. Decreto No. 10.474 de 26 de agosto de 2020. Estructura de la Autoridade Nacional de Proteção de Dados (ANPD). Diário Oficial da União, 2020.

Brazil. Lei No. 12.965, Marco Civil da Internet. Diário Oficial da União, 2014.

Brazil. Lei No. 13.709. Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União, 2018.

Brazil. Lei No. 13.853. Modifica la Lei Geral de Proteção de Dados Pessoais. Diário Oficial da União, 2019.

Brazil. Estratégia Brasileira de Inteligência Artificial (EBIA). Brasília: Ministério da Ciência, Tecnologia e Inovações, 2021.

Brazil. Projeto de Lei No. 2338/2023. Marco Legal da Inteligência Artificial. Senado Federal, 2023.

 

Chile

Chile. Ley No. 19.628 sobre Protección de la Vida Privada. Diario Oficial de la República de Chile, 1999.

Chile. Ley No. 21.719 Regula la Protección y Tratamiento de Datos Personales y Crea la Agencia de Protección de Datos Personales. Diario Oficial de la República de Chile, 2024.

Chile. Política Nacional de Inteligencia Artificial. Santiago: Ministerio de Ciencia, Tecnología, Conocimiento e Innovación, 2021 (updated 2024).

Chile. Proyecto de Ley 15935-07. Modifica el Código Penal para Sancionar el Mal Uso de la Inteligencia Artificial. Cámara de Diputados de Chile, 2023.

Chile. Proyecto de Ley 16021-07. Modifica el Código Penal para Incorporar el Uso de Inteligencia Artificial como Circunstancia Agravante. Cámara de Diputados de Chile, 2023.

Chile. Proyecto de Ley 16821-19. Regula los sistemas de inteligencia artificial. Cámara de Diputados de Chile, 2024.

 

Mexico

Mexico. Ley Federal de Protección de Datos Personales en Posesión de los Particulares. Diario Oficial de la Federación, 2010.

Mexico. Iniciativa de Reforma a la Ley Federal del Derecho de Autor en Materia de Inteligencia Artificial. Cámara de Diputados, 2024.

Mexico. Iniciativa de Reforma a la Ley General de Salud en Materia de Inteligencia Artificial Aplicada a la Salud. Cámara de Diputados, 2024.

Mexico. Iniciativa para Establecer el Deber de los Patrones de Capacitar en Inteligencia Artificial. Senado de la República, 2024.

Mexico. Iniciativa para Definir los Sistemas de Inteligencia Artificial en la Ley. Senado de la República, 2024.

Mexico. Iniciativa para Establecer una Nueva Ley Marco sobre Inteligencia Artificial. Senado de la República, 2024.

Mexico. Proyecto de Ley que Establece el Tipo Penal para el Uso de Identidad mediante Inteligencia Artificial. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Incorpora la Violencia Sexual mediante Suplantación de Identidad con Inteligencia Artificial. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Tipifica el Uso Malintencionado de la Inteligencia Artificial (Deepfakes, Fraude, etc.). Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Tipifica la Violencia a la Intimidad Sexual mediante Inteligencia Artificial. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Establece la Violencia Digital con Inteligencia Artificial como Modalidad de Violencia contra la Mujer. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Propone Adicionar el Delito de Violación a la Intimidad con Imágenes Manipuladas mediante Inteligencia Artificial. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Incorpora como Violencia Digital las Imágenes Manipuladas por Inteligencia Artificial. Cámara de Diputados, 2023.

Mexico. Proyecto de Ley que Reconoce el Uso de IA Generativa como Modalidad de Violencia hacia las Mujeres. Cámara de Diputados, 2023.