The thesis explores the complexities and risks associated with Automated Decision-Making (ADM) systems in social welfare, focusing on the phenomenon of ”digital cages.” The digital cage concept refers to a situation where rigid systems, perpetuated through algorithmic misclassification, inadvertently trap citizens in bureaucratic complications without recourse. This thesis analyzes the digital cages concept from a system safety perspective, on the example of the Dutch Toeslagenaffair. The thesis centers on understanding how digital cages form within social welfare systems and seeks methods to mitigate their emergence through targeted interventions using system safety theory. The pivotal question addressed is: ”What are safety control interventions to curb the emergence of algorithmic decision-making systems-induced digital cages in the context of national social welfare administration in the Netherlands?” The research concludes by highlighting the potential of assumption-based leading indicators. Assumptionbased leading indicators are predictive measures that rely on underlying assumptions about how certain inputs or actions correlate with future outcomes. The leading indicators provide a novel approach to enhancing system safety in social welfare administration by addressing the underlying assumptions of the system in operation. This offers a substantial potential to transform how social welfare systems manage and utilize ADM, potentially reducing the incidence of digital cages. By detecting early signs of hazardous systems states digital cages can be mitigated before they solidify into systemic issues. The thesis not only addresses the immediate concerns regarding digital cages but also opens pathways for future inquiries into safer and more equitable administrative practices. Future research is suggested to further refine these indicators and explore their applicability in diverse administrative contexts, ensuring they can effectively adapt to the evolving landscape of public administration and technology.