Discovering bias and fairness evaluation methods
Fairness and bias are opposing concepts. Fairness seeks to ensure fair and equal treatment in decision-making for all individuals or groups, while bias refers to unfair or unequal treatment. Mitigating bias is a crucial step in achieving fairness. Bias can exist in different forms and addressing all potential biases is complicated. Additionally, it’s important to understand that achieving fairness in one aspect doesn’t guarantee the complete absence of bias in general.
To understand both how much bias and how fair our data and model are, what we need is a set of bias and fairness metrics to objectively measure and evaluate. This will then enable a feedback mechanism to iteratively and objectively mitigate bias and achieve fairness. Let’s go through a few robust bias and fairness metrics that you need to have in your arsenal of tools to achieve fairness:
