Fair Models in Credit: Intersectional Discrimination and the
  Amplification of Inequity
Savina Kim Stefan Lessmann Galina Andreeva Michael Rovatsos
The increasing usage of new data sources and machine learning (ML) technology
in credit modeling raises concerns with regards to potentially unfair
decision-making that rely on protected characteristics (e.g., race, sex, age)
or other socio-economic and demographic data. The authors demonstrate the
impact of such algorithmic bias in the microfinance context. Difficulties in
assessing credit are disproportionately experienced among vulnerable groups,
however, very little is known about inequities in credit allocation between
groups defined, not only by single, but by multiple and intersecting social
categories. Drawing from the intersectionality paradigm, the study examines
intersectional horizontal inequities in credit access by gender, age, marital
status, single parent status and number of children. This paper utilizes data
from the Spanish microfinance market as its context to demonstrate how
pluralistic realities and intersectional identities can shape patterns of
credit allocation when using automated decision-making systems. With ML
technology being oblivious to societal good or bad, we find that a more
thorough examination of intersectionality can enhance the algorithmic fairness
lens to more authentically empower action for equitable outcomes and present a
fairer path forward. We demonstrate that while on a high-level, fairness may
exist superficially, unfairness can exacerbate at lower levels given
combinatorial effects; in other words, the core fairness problem may be more
complicated than current literature demonstrates. We find that in addition to
legally protected characteristics, sensitive attributes such as single parent
status and number of children can result in imbalanced harm. We discuss the
implications of these findings for the financial services industry.