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Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a machine learning model that uses cost-sensitive learning is proposed to automate the task of handing out credit card limit increases. To accomplish this, several neural network and XGBoost models are trained and compared, leveraging Venezolano de Credito's data and using grid search with 10-fold cross-validation. The proposed model is ultimately chosen due to its superior balance of accuracy, cost-effectiveness, and interpretability. The model's performance is evaluated against the committee's decisions using Cohen's kappa coefficient, showing an almost perfect agreement.

The current hype around artificial intelligence (AI) conceals the substantial human intervention underlying its development. This article lifts the veil on the precarious and low-paid 'data workers' who prepare data to train, test, check, and otherwise support models in the shadow of globalized AI production. We use original questionnaire and interview data collected from 220 workers in Argentina (2021-22), 477 in Brazil (2023), and 214 in Venezuela (2021-22). We compare them to detect common patterns and reveal the specificities of data work in Latin America, while disclosing its role in AI production.We show that data work is intertwined with economic hardship, inequalities, and informality. Despite workers' high educational attainment, disadvantage is widespread, though with cross-country disparities. By acknowledging the interconnections between AI development, data work, and globalized production, we provide insights for the regulation of AI and the future of work, aiming to achieve positive outcomes for all stakeholders.

Bahar and Hausmann (2025a) claim to find evidence against the hypothesis that oil sanctions on Venezuela lead to increased migration flows to the United States. We show that their findings derive from applying a nonstandard, misspecified Engle-Granger test to first differences. This specification is incorrect because cointegration tests are designed to evaluate relationships between the levels of variables, not their first differences. Since the residuals from regressions of I(0) variables will, under general conditions, be stationary, testing for cointegration between first differences of I(1) variables virtually ensures a spurious finding of cointegration. Using Monte Carlo simulations, we show that the misspecified Bahar-Hausmann test on first differences exhibits a false positive rate of 100 percent. Once the Engle-Granger test is applied correctly to the logarithms of levels, the evidence of cointegration vanishes. The Bahar-Hausmann regressions therefore provide no valid basis for inference about any underlying relationship between migration and Venezuelan oil revenues.

We investigate how government-orchestrated assaults on the judiciary, disguised as modernization efforts, undermine judicial independence. Our study focuses on Venezuela's constitutional overhaul in the early 2000s, initiated by Hugo Chávez and implemented through a judicial emergency committee. We employ a hybrid synthetic control and difference-in-differences approach to estimate the impact of populist attacks on judicial independence trajectories. By comparing Venezuela to a stable pool of countries without radical constitutional changes, our identification strategy isolates the effect of populist assaults from unobservable confounders and common time trends. Our findings reveal that authoritarian interventions lead to an immediate and lasting breakdown of judicial independence. The deterioration in judicial independence vis-á-vis the estimated counterfactual is robust to variations in the donor pool composition. It does not appear to be driven by pre-existing judicial changes and withstands numerous temporal and spatial placebo checks across over nine million randomly sequenced donor samples.

This article examines the organisational and geographical forces that shape the supply chains of artificial intelligence (AI) through outsourced and offshored data work. Bridging sociological theories of relational inequalities and embeddedness with critical approaches to Global Value Chains, we conduct a global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela. The AI supply chains procure data work via a mix of arm's length contracts through marketplace-like platforms, and of embedded firm-like structures that offer greater stability but less flexibility, with multiple intermediate arrangements. Each solution suits specific types and purposes of data work in AI preparation, verification, and impersonation. While all forms reproduce well-known patterns of exclusion that harm externalised workers especially in the Global South, disadvantage manifests unevenly in different supply chain structures, with repercussions on remunerations, job security and working conditions. Unveiling these processes of contemporary technology development provides insights into possible policy implications.

Labor plays a major, albeit largely unrecognized role in the development of artificial intelligence. Machine learning algorithms are predicated on data-intensive processes that rely on humans to execute repetitive and difficult-to-automate, but no less essential, tasks such as labeling images, sorting items in lists, recording voice samples, and transcribing audio files. Online platforms and networks of subcontractors recruit data workers to execute such tasks in the shadow of AI production, often in lower-income countries with long-standing traditions of informality and lessregulated labor markets. This study unveils the resulting complexities by comparing the working conditions and the profiles of data workers in Venezuela, Brazil, Madagascar, and as an example of a richer country, France. By leveraging original data collected over the years 2018-2023 via a mixed-method design, we highlight how the cross-country supply chains that link data workers to core AI production sites are reminiscent of colonial relationships, maintain historical economic dependencies, and generate inequalities that compound with those inherited from the past. The results also point to the importance of l

This paper examines the potential impact of different US economic sanctions policies on Venezuelan migration flows. I consider three possible departures from the current status quo in which selected oil companies are permitted to conduct transactions with Venezuela's state-owned oil sector: a return to maximum pressure, characterized by intensive use of secondary sanctions, a more limited tightening that would revoke only the current Chevron license, and a complete lifting of economic sanctions. I find that sanctions significantly influence migration patterns by disrupting oil revenues, which fund imports critical to productivity in the non-oil sector. Reimposing maximum pressure sanctions would lead to an estimated one million additional Venezuelans emigrating over the next five years compared to a baseline scenario of no economic sanctions. If the US aims to address the Venezuelan migrant crisis effectively, a policy of engagement and lifting economic sanctions appears more likely to stabilize migration flows than a return to maximum pressure strategies.

Many of the world's workers rely on digital platforms for their income. In Venezuela, a nation grappling with extreme inflation and where most of the workforce is self-employed, data production platforms for machine learning have emerged as a viable opportunity for many to earn a flexible income in US dollars. Platform workers are deeply interconnected within a vast network of firms and entities that act as intermediaries for wage payments in digital currencies and its subsequent conversion to the national currency, the bolivar. Past research on embeddedness has noted that being intertwined in multi-tiered socioeconomic networks of companies and individuals can offer significant rewards to social participants, while also connoting a particular set of limitations. This paper furnishes qualitative evidence regarding how this deep embeddedness impacts platform workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through various financial platforms include access to a more stable currency and the ability to save and invest outside the national financial system. However, relying on numerous digital and local intermediarie

Since 2018, Twitter has steadily released into the public domain content discovered on the platform and believed to be associated with information operations originating from more than a dozen state-backed organizations. Leveraging this dataset, we explore inter-state coordination amongst state-backed information operations and find evidence of intentional, strategic interaction amongst thirteen different states, separate and distinct from within-state operations. We find that coordinated, inter-state information operations attract greater engagement than baseline information operations and appear to come online in service to specific aims. We explore these ideas in depth through two case studies on the coordination between Cuba and Venezuela, and between Russia and Iran.

Urban environments are intricate systems where the breakdown of critical infrastructure can impact both the economic and social well-being of communities. Electricity systems hold particular significance, as they are essential for other infrastructure, and disruptions can trigger widespread consequences. Typically, assessing electricity availability requires ground-level data, a challenge in conflict zones and regions with limited access. This study shows how satellite imagery, social media, and information extraction can monitor blackouts and their perceived causes. Night-time light data (in March 2019 for Caracas, Venezuela) is used to indicate blackout regions. Twitter data is used to determine sentiment and topic trends, while statistical analysis and topic modeling delved into public perceptions regarding blackout causes. The findings show an inverse relationship between nighttime light intensity. Tweets mentioning the Venezuelan President displayed heightened negativity and a greater prevalence of blame-related terms, suggesting a perception of government accountability for the outages.

Humanitarian agencies must be prepared to mobilize quickly in response to complex emergencies, and their effectiveness depends on their ability to identify, anticipate, and prepare for future needs. These are typically highly uncertain situations in which predictive modeling tools can be useful but challenging to build. To better understand the need for humanitarian support -- including shelter and assistance -- and strengthen contingency planning and protection efforts for displaced populations, we present a situational analysis tool to help anticipate the number of migrants and forcibly displaced persons that will cross a border in a humanitarian crisis. The tool consists of: (i) indicators of potential intent to move drawn from traditional and big data sources; (ii) predictive models for forecasting possible future movements; and (iii) a simulation of border crossings and shelter capacity requirements under different conditions. This tool has been specifically adapted to contingency planning in settings of high uncertainty, with an application to the Brazil-Venezuela border during the COVID-19 pandemic.

Objective: The aim of this research is to demonstrate how the use of hierarchical cluster analysis on 366 municipalities and other minor entities (parishes) of Venezuela, could be useful to consider regional differences and similarities between territorial entities when designing national public policies of Water, Sanitation and Hygiene (WASH) based on evidence. Methods and results: Consider data from various sources to characterize the population of Venezuela through their territorial entities. Select variables at the level of the territorial entities to cover demographic characteristics, mortality and nutrition, coverage of reliable water and sanitation services, access to education, and access to information and communication technologies. Classify the territorial entities into a limited number of mutually exclusive groups using hierarchical clustering techniques and based on proximity in the multi-dimensional space. Adjust of assignments, reallocating some entities into a different group based on the specialists' opinion about its hierarchy in the cities regional system and its geographic location. Define an indicator to verify the consistency of the groups built. Conduct a sta

Venezuela has suffered three economic catastrophes since independence: one each in the nineteenth, twentieth, and twenty-first centuries. Prominent explanations for this trilogy point to the interaction of class conflict and resource dependence. We turn attention to intra-class conflict, arguing that the most destructive policy choices stemmed not from the rich defending themselves against the masses but rather from pitched battles among elites. Others posit that Venezuelan political institutions failed to sustain growth because they were insufficiently inclusive; we suggest in addition that they inadequately mediated intra-elite conflict.

We revisit the results of a recent paper by Equipo Anova, who claim to find evidence of an improvement in Venezuelan imports of food and medicines associated with the adoption of U.S. financial sanctions towards Venezuela in 2017. We show that their results are consequence of data coding errors and questionable methodological choices, including the use an unreasonable functional form that implies a counterfactual of negative imports in the absence of sanctions, the omission of data accounting for four-fifths of the country's food imports at the time of sanctions and incorrect application of regression discontinuity methods. Once these errors are corrected, the evidence of a significant improvement in the level and rate of change in imports of essentials disappears.

State-sponsored online influence operations typically consist of coordinated accounts exploiting the online space to influence public opinion. Accounts associated with these operations use images and memes as part of their content generation and dissemination strategy to increase the effectiveness and engagement of the content. In this paper, we present a study of images from the PhoMemes 2022 Challenge originating from the countries China, Iran, Russia, and Venezuela. First, we analyze the coordination of images within and across each country by quantifying image similarity. Then, we construct Image-Image networks and image clusters to identify key themes in the image influence operations. We derive the corresponding Account-Account networks to visualize the interaction between participating accounts within each country. Finally, we interpret the image content and network structure in the broader context of the organization and structure of influence operations in each country.

This work advances investigations into the visual media shared by agents in disinformation campaigns by characterizing the images shared by accounts identified by Twitter as being part of such campaigns. Using images shared by US politicians' Twitter accounts as a baseline and training set, we build models for inferring the ideological presentation of accounts using the images they share. Results show that, while our models recover the expected bimodal ideological distribution of US politicians, we find that, on average, four separate influence campaigns -- attributed to Iran, Russia, China, and Venezuela -- all present conservative ideological presentations in the images they share. Given that prior work has shown Twitter accounts used by Russian disinformation agents are ideologically diverse in the text and news they share, these image-oriented findings provide new insights into potential axes of coordination and suggest these accounts may not present consistent ideological positions across modalities.

The global cobalt supply chain is more interconnected—and more vulnerable—than previously thought, with disruptions capable of triggering far-reaching cascades across multiple countries and industries。 Researchers warn that protecting battery supply chains will require system-wide coordination because critical bottlenecks can turn local shocks into

Using the Keck Observatory, astronomers measured the spins of dozens of giant planets and brown dwarfs orbiting distant stars。 They found that giant planets can spin faster than much more massive brown dwarfs, challenging simple assumptions about mass and rotation。 The results suggest that magnetic fields and formation processes play a major role i

NASA’s quiet supersonic flight tests could eventually go on a national tour