Fairness Through Unawareness is an approach to machine learning and decision-making that aims to prevent bias by excluding sensitive attributes such as race, gender, or age from the decision-making process. However, this method is often criticized for its ineffectiveness, as proxies for these attributes can still inadvertently influence outcomes, failing to address structural biases embedded in data.