Evaluating the resilience of AI-based predictive models in the context of economic crises: a systematic review
DOI:
https://doi.org/10.5281/zenodo.17382855Keywords:
economic crises, artificial intelligence, resilienceAbstract
In a context of growing economic uncertainty, understanding how crises impact the resilience of artificial intelligence (AI)-based predictive models is fundamental. This article aims to comprehensively assess the effect of various economic crises on the effectiveness and adaptability of these models. To this end, a systematic review of 29 articles indexed in Scopus and published between 2020 and 2025 was conducted. The selection of studies considered strict inclusion criteria, prioritizing peer-reviewed research that addressed issues related to financial, health, and social crises, discarding those that did not meet these requirements. The results reveal that models that incorporate macroeconomic and contextual variables present greater robustness and accuracy, standing out for their ability to adapt to highly volatile scenarios. Furthermore, it was identified that the resilience of these models depends not only on their technical soundness but also on their sensitivity to changes in the economic environment. Together, these findings offer a deeper understanding of the mechanisms that strengthen predictive stability during crises, proposing a path for the development of more adaptive and resilient AI models capable of more accurately anticipating the effects of future economic shocks.
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