Societal Biases Reinforcement Through Machine Learning – A Credit Scoring Perspective
To appear in AI and Ethics
14 Pages Posted: 9 Jul 2020 Last revised: 2 Nov 2020
Date Written: June 12, 2020
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.
Keywords: SMOTE, Machine Learning, Social Bias, Credit Scoring, Random Forest
JEL Classification: C60, C80, G21, G41
Suggested Citation: Suggested Citation