Algorithms Against Corruption? Why AI Succeeds in Brazil and Chile but Stumbles in Venezuela

Faculty Mentor

Aiysha Varraich

Location

Russell Union Room 2054

Type of Research

On-going

Session Format

Oral Presentation

College

College of Behavioral & Social Sciences

Department

Political Science and International Studies

Abstract

This study considers how AI's effectiveness depends on political, economic, and institutional contexts. It argues that while AI can be a helpful tool for improving accountability, it is not a universal solution; its impact is shaped by the conditions under which it is implemented. In Brazil and Chile, where public procurement systems are extensive, data is accessible, and oversight institutions retain relative autonomy, AI tools have successfully supported transparency. Brazil’s Alice system for instance reviews daily procurement transactions to identify irregularities and generate alerts, illustrating how technology can reinforce institutional integrity when conditions are favorable. Venezuela highlights the constraints of weak governance, despite investments in digital infrastructure and governance, corruption, political capture, and restricted access to data undermine the capacity of AI to effectively operate. Recent political changes and ongoing uncertainty in Venezuela’s governance landscape further complicate AI implementation, as institutional instability and contested authority limit the reliability, independence, and enforcement capacity necessary for data driven oversight. Instead of fostering accountability, technological systems risk being co-opted to entrench existing power structures. This suggests that AI’s success is not just about technology; it depends on the political openness, economic structures, and institutional capacity surrounding its use. The goal of this study is to identify the political and institutional conditions that shape the success or failure of AI driven anti-corruption initiatives. Using a comparative and context sensitive design, the study moves beyond technological determinism by examining how AI performance is mediated by governance structures and power relations. This study contributes to the understanding of the interactions between emerging technologies and entrenched corruption systems, offering ideas for the design and deployment of AI tools in diverse governance settings.

Program Description

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Start Date

4-23-2026 1:45 PM

End Date

4-23-2026 2:00 PM

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Apr 23rd, 1:45 PM Apr 23rd, 2:00 PM

Algorithms Against Corruption? Why AI Succeeds in Brazil and Chile but Stumbles in Venezuela

Russell Union Room 2054

This study considers how AI's effectiveness depends on political, economic, and institutional contexts. It argues that while AI can be a helpful tool for improving accountability, it is not a universal solution; its impact is shaped by the conditions under which it is implemented. In Brazil and Chile, where public procurement systems are extensive, data is accessible, and oversight institutions retain relative autonomy, AI tools have successfully supported transparency. Brazil’s Alice system for instance reviews daily procurement transactions to identify irregularities and generate alerts, illustrating how technology can reinforce institutional integrity when conditions are favorable. Venezuela highlights the constraints of weak governance, despite investments in digital infrastructure and governance, corruption, political capture, and restricted access to data undermine the capacity of AI to effectively operate. Recent political changes and ongoing uncertainty in Venezuela’s governance landscape further complicate AI implementation, as institutional instability and contested authority limit the reliability, independence, and enforcement capacity necessary for data driven oversight. Instead of fostering accountability, technological systems risk being co-opted to entrench existing power structures. This suggests that AI’s success is not just about technology; it depends on the political openness, economic structures, and institutional capacity surrounding its use. The goal of this study is to identify the political and institutional conditions that shape the success or failure of AI driven anti-corruption initiatives. Using a comparative and context sensitive design, the study moves beyond technological determinism by examining how AI performance is mediated by governance structures and power relations. This study contributes to the understanding of the interactions between emerging technologies and entrenched corruption systems, offering ideas for the design and deployment of AI tools in diverse governance settings.