The Paradox of Algorithmic Resilience
Year: 2026
Type: article
Abstract: This presentation examines the relationship between algorithmic literacy, digital awareness, digital trust, digital cynicism, critical thinking, and public resilience against AI-driven disinformation in Indonesia. It explores whether greater understanding of digital algorithms strengthens citizens' capacity to resist manipulation or may simultaneously contribute to distrust, cynicism, and disengagement. The study employs a cross-sectional survey of 116 Indonesian social media users conducted between January and July 2026. Partial Least Squares Structural Equation Modelling (PLS-SEM) is used to examine relationships among cognitive factors, affective mediators, and behavioural resilience, including proposed moderating effects of AI exposure intensity and critical thinking. The findings indicate that algorithmic literacy and digital awareness are positively associated with both digital trust and digital cynicism. However, neither trust nor cynicism significantly predicts public resilience, and the hypothesised mediation and moderation effects are not statistically supported. Critical thinking emerges as the only statistically significant predictor of public resilience in the reported model (β = 0.357, p = 0.001), while algorithmic literacy and digital awareness do not demonstrate significant direct associations with resilience. The presentation highlights the limitations of approaches focused exclusively on digital literacy and proposes a multidimensional governance framework involving public institutions, digital platforms, media organisations, and citizens. Particular emphasis is placed on analytical reasoning, evidence verification, and practical agency in responding to synthetic content and disinformation. Given the limited sample size and identified measurement validity concerns, the findings are exploratory and require further validation through larger studies with improved measurement instruments.
Institutions: Universitas Panca Marga, Jenderal Soedirman University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225625221
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