Detecting bias

There are many sources for bias in machine learning. As outlined in Chapter 1, Interpretation, Interpretability, and Explainability; and Why Does It All Matter?, there are ample sources of bias. Those rooted in the truths that the data is representing, such as systemic and structural ones that lead to prejudice bias in the data. There are also biases rooted in the data itself, such as sample, exclusion, association, and measurement biases. Lastly, there are biases in the insights we derive from data or models we have to be careful with, such as conservatism bias, salience bias, and fundamental attribution error.

For this example, to properly disentangle so many bias levels, we ought to connect our data to census data for Taiwan in 2005 and historical lending data split by demographics. Then, using these external datasets, control for credit card contract conditions, as well as gender, income, and other demographic data to ascertain if young people, in particular,...