Case Study: Identify Bias in Model Training

Have you ever wondered how discrimination can still exist in the field of credit loans despite regulatory norms that explicitly prohibit it? Research shows that sex and race can play a role in loan approval, with men having a higher chance of getting approved compared to women, even when all other factors are the same. This sex discrimination is also reflected in the loan amounts approved.

To make matters worse, automated decision-making systems, like machine learning models, are now commonly used in financial lending, which can exacerbate these disparities.

In this case study, imagine you’re a data scientist working for a financial institution. Your task is to develop a machine learning model that predicts whether an applicant will default on a personal loan. If the model predicts a positive outcome, it means the applicant is likely to default, which can have serious consequences for the client.

To ensure fairness in our model, we use the Fairlearn library, which provides various metrics for assessing fairness. We want to identify and address any gender-based differences in financial lending decisions.

Finally, we implement strategies to mitigate any unfairness in our model and compare the results to the original model. This way, we can see if our efforts to promote fairness have had a positive impact.

Isn’t it interesting how machine learning can help us uncover and address biases in the financial lending process? Let’s dive deeper into this case study and explore how we can create fairer and more equitable systems.