To address discrimination and inequality in automated decision making systems it is standard practice to implement so-called ``fairness" metrics during algorithm design. These measures, although useful to enforce and diagnose fairness at the decision stage, are not sufficient to capture forms of discrimination arising throughout and from structural properties of the system as a whole. To complement the standard approach, we propose a systemic analysis, aided by structural causal models, through which social interventions can be compared to algorithmic interventions. This framework allows us to identify bias outside the algorithmic stage, and propose joint interventions on social dynamics and algorithm design. We show how, for a model of financial lending, structural interventions can drive the system towards equality even when algorithmic interventions are not able to do so. This means the responsibility of decision makers does not stop when local fairness metrics are satisfied, they must ensure a whole ecosystem that fosters equity for all.