Fairness Through Unawareness is a concept in algorithmic fairness where sensitive attributes, such as race or gender, are excluded from the data used by a model. The idea is that by not including these attributes, the model cannot discriminate based on them. However, this approach is flawed because other features (proxies) can still indirectly encode sensitive information, leading to biased outcomes. In cybersecurity and machine learning, relying solely on unawareness can perpetuate existing biases, as models may still learn discriminatory patterns from correlated data. Thus, true fairness requires more proactive strategies to identify and mitigate bias beyond just removing sensitive fields.