Societal biases reinforcement through machine learning: A credit scoring perspective

TL;DR

This paper investigates how social biases related to gender and ethnicity are embedded and reinforced in machine learning models used for credit scoring, highlighting the reflection of societal prejudices in algorithmic decisions.

Contribution

It provides an analysis of bias transmission from societal data to credit scoring models, emphasizing the reinforcement of social biases through machine learning algorithms.

Findings

Biases related to gender and ethnicity are reflected in credit scoring models.

Machine learning models tend to reinforce societal prejudices present in training data.

The study highlights the importance of addressing social biases in AI-driven credit decisions.

Abstract

Does machine learning and AI ensure that social biases thrive ? This paper aims to analyse this issue. Indeed, as algorithms are informed by data, if these are corrupted, from a social bias perspective, good machine learning algorithms would learn from the data provided and reverberate the patterns learnt on the predictions related to either the classification or the regression intended. In other words, the way society behaves whether positively or negatively, would necessarily be reflected by the models. In this paper, we analyse how social biases are transmitted from the data into banks loan approvals by predicting either the gender or the ethnicity of the customers using the exact same information provided by customers through their applications.