“Driving financial discrimination against the marginalized: Algorithms have long been used to create credit scores and inform loan screening. However, with the rise of big data, systems are now using machine learning to incorporate and analyze non-financial data points to determine creditworthiness, from where people live, to their internet browsing habits, to their purchasing decisions. The outputs these systems produce are known as e-scores, and unlike formal credit scores they are largely unregulated. As data scientist Cathy O’Neil has pointed out, these scores are often discriminatory and create pernicious feedback loops.4” ([Access Now, 2018, p. 16](zotero://select/groups/4907410/items/KIU6F9PR)) ([pdf](zotero://open-pdf/groups/4907410/items/598C5P37?page=16&annotation=GTXRLF2L))