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.

使用机器学习在信用建模中的新数据来源引发了涉及受保护特征（例如种族、性别、年龄）或其他社会经济与人口统计数据的不公平决策的担忧。本文通过微金融背景展示了此类算法偏见的影响，并综合干预性的视角研究了以性别、年龄、婚姻状况、单亲状态和子女数量为定义的不同群体之间的信用分配不公平问题。我们发现在多元社会中应用自动化决策系统时，交叉性别、年龄和家庭状况的身份会影响信用分配模式，而机器学习技术对社会公平与不公平视而不见。因此，更全面地考虑交叉身份可以增强算法公平性的视角，更真实地推动平等结果，并提供更公正的前进路径。同时，我们发现除了受法律保护的特征外，像单亲状态和子女数量这样的敏感属性也可能导致不平衡的伤害。本研究对金融服务行业的影响进行了讨论。