Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination

Machine Learning is a vital part of various modern day decision making software. At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes.

Automated summary

Machine Learning plays a crucial role in modern decision-making software, but bias is a concern. Achieving fairness involves treating sensitive attributes equally, while also considering discrimination against anti-protected attributes. By using grid search, it is possible to reduce bias while improving the ability to discriminate against anti-protected attributes.