ropenscilabs/proxy-bias-vignette

Machine Learning systems often inherit biases against protected classes and historically disparaged groups via their training data [1]. Though some biases in features are straightforward to detect (ex: age, gender, race), others are not explicit and rely on subtle correlations in machine learning algorithms to understand. The incorporation of unintended bias into predictive models is called proxy discrimination. In this vignette, we will be implementing an example machine learning model using decision trees, and determining whether its classification for loan recipients is biased against certain groups. We will explore several ways of detecting unintentional bias and removing it from our predictive model.