<![CDATA[Background: Algorithmic governance is reshaping social protection, yet the distributive consequences of automated welfare targeting in the Global South remain poorly understood. The study was grounded in street-level bureaucracy and procedural-justice theory. 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 with a cross-sectional survey of 640 household heads served by a public organization in Palembang, South Sumatera, Indonesia (response rate 84.2%), and phenomenological interviews with false-negative cases. Results: All scales were reliable (Cronbach's alpha 0.79-0.88). The audit found 72.3% aggregate accuracy and a 23.4% false-negative rate, rising to 55.1% among undocumented households. Employment informality (beta=0.28), misclassification (beta=0.31), and digital transaction visibility (beta=0.19) positively predicted exclusion, while procedural justice was protective (beta=-0.22); the model explained 52% of variance (F(9,630)=75.84, p<0.001). Misclassification partially mediated the informality-exclusion link (indirect effect=0.17, 95% CI [0.12, 0.23]), procedural justice buffered it (interaction beta=-0.14, p=0.002), and non-digital informal workers had 4.27 times higher exclusion odds (95% CI [2.98, 6.12]). Interviews showed that gross e-wallet throughput was misread as income and frontline discretion collapsed into a computer-says-no bureaucracy. Conclusion: Algorithmic unfairness in Indonesia reflects parameterization bias against informal livelihoods. Welfare systems should restore conditional human-in-the-loop discretion and accessible appeal mechanisms.]]>
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