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Abstract
Background: Algorithmic governance is reshaping social protection, but the distributive consequences of automated welfare targeting in the Global South remain poorly understood.
Objective: To examine whether digital transaction visibility and employment informality predict false-negative welfare exclusion, whether perceived algorithmic misclassification mediates these effects, and whether algorithmic procedural justice moderates them.
Methods: An explanatory-sequential mixed-methods design combined an audit of 2,500 automated eligibility decisions, a cross-sectional survey of 640 household heads in Palembang, Indonesia, and phenomenological interviews with false-negative cases.
Results: The audit found 72.3% accuracy and a 23.4% false-negative rate. Informality, misclassification, and digital visibility predicted exclusion, while procedural justice was protective; the model explained 52% of variance. Misclassification partially mediated the association, and non-digital informal workers had 4.27 times higher odds of exclusion.
Conclusion: Algorithmic unfairness reflected parameterization bias against informal livelihoods, supporting conditional human oversight and accessible appeal mechanisms.
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