Case study

The challenge at hand centers on uncovering and addressing potential bias within a dataset pertaining to credit card defaults in Taiwan. Acquired from the UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients), this dataset comprises information from 30,000 credit card clients over a six-month span, including demographic factors such as gender, marital status, and education. The key concern is whether these demographic features introduce bias into a decision tree classifier trained on all available features, with a specific focus on gender-related bias. The overarching objective of this example is to not only identify but also mitigate any biased outcomes through the application of data-centric techniques. By reevaluating the algorithm’s performance using fairness metrics, the example aims to shed light on the real-world implications of bias in financial decision-making, particularly how these biases can impact...