2 Easy Ways To Avoid Racial Discrimination in Your Model

A high-level goal of many AI projects is to address the ethical implications of algorithms along the lines of fairness and discrimination. It is a known fact that algorithms can facilitate illegal discrimination. For example, it may not surprise that each investor wants to put more capital in loans with a high return of investment and low risk. A modern idea is to use a machine learning model to decide, based on the sliver of known information about the outcome of past loans, which future loan requests give the largest chance of the borrower fully paying it back while achieving the best trade-off with high returns (high-interest rate). There's one problem: the model is trained on historical data, and poor uneducated people, often racial minorities or people with less working experience have a historical trend of being more likely to succumb to loan charge-off than the general population.