Abstract

This TREO talk explores a comparative, qualitative pilot study based on secondary data on AI’s potential socio-economic impact, structured around four proposed metrics: Employment, Economic Competition, Societal Stability, and AI Preparedness. These dimensions were selected to capture both economic exposure and institutional capacity, drawing from existing frameworks in development economics and AI policy. The study seeks to answer the research question: What threats and opportunities does Artificial Intelligence (AI) present to populations in developing economies? The theoretical basis for this paper is grounded in technological affordances theory (Majchrzack & Markus, 2012). Applying this theory lens demonstrates that the innovative potential of AI threatens the value of human capital, particularly those with low technical skills or little education (Maragno et al., 2023). Moreover, developing and implementing AI to optimize manufacturing processes (Arinez et al., 2020), particularly in technologically advanced economies, may facilitate protectionist trade policies and render domestic production competitive with inexpensive foreign labor. Furthermore, democratic backsliding, social instability, and limited state capacity create unique vulnerabilities in the Global South. The proposed framework evaluates the research question through a technological affordances lens by comparing the cases of Mexico and Argentina. These countries were selected due to their contrasting levels of economic openness and starkly different socio-political circumstances. Pilot study findings suggest Argentina’s economic instability and shrinking state capacity risk magnifying job displacement and democratic erosion. At the same time, Mexico’s export-oriented economy and stronger digital base offer more resilience, albeit with vulnerabilities in education and infrastructure. This TREO session invites discussion on not only the implications of the findings, but also how both to improve the conceptual grounding of the measures employed in the study and to expand the pilot study to scale. Specifically, this session welcomes critical feedback on the relevance and development of the four metrics, including their connection to existing and emerging literature on AI’s impacts in the Global South. Moreover, it seeks to further develop and extend the pilot study by incorporating quantitative research methods to complement the qualitative pilot study. This extension enables the generation of further empirical findings and the replication of a similar comparative approach for other developing nations. By establishing an empirically sound framework, findings can be tailored into appropriate policy solutions to address the impacts of AI for a country’s particular circumstances.

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