Societal biases reinforcement through machine learning: A credit scoring perspective
This addresses the problem of algorithmic bias in financial services, which can perpetuate discrimination against marginalized groups, and is incremental as it applies known bias analysis to credit scoring.
The paper investigates how societal biases in data can be reinforced by machine learning algorithms, specifically in credit scoring, by showing that models can predict gender or ethnicity from customer application information, leading to biased loan approvals.
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.