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The increasing usage of new data sources and machine learning (ML) technologyin credit modeling raises concerns with regards to potentially unfairdecision-making that rely on protected characteristics (e.g., race, sex, age)or other socio-economic and demographic data. The authors demonstrate theimpact of such algorithmic bias in the microfinance context. Difficulties inassessing credit are disproportionately experienced among vulnerable groups,however, very little is known about inequities in credit allocation betweengroups defined, not only by single, but by multiple and intersecting socialcategories. Drawing from the intersectionality paradigm, the study examinesintersectional horizontal inequities in credit access by gender, age, maritalstatus, single parent status and number of children. This paper utilizes datafrom the Spanish microfinance market as its context to demonstrate howpluralistic realities and intersectional identities can shape patterns ofcredit allocation when using automated decision-making systems. With MLtechnology being oblivious to societal good or bad, we find that a morethorough examination of intersectionality can enhance the algorithmic fairnesslens to more authentically empower action for equitable outcomes and present afairer path forward. We demonstrate that while on a high-level, fairness mayexist superficially, unfairness can exacerbate at lower levels givencombinatorial effects; in other words, the core fairness problem may be morecomplicated than current literature demonstrates. We find that in addition tolegally protected characteristics, sensitive attributes such as single parentstatus and number of children can result in imbalanced harm. We discuss theimplications of these findings for the financial services industry.

Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity