Learning new representations of data to reduce correlation with sensitive attributes is one method to tackle algorithmic bias. In this paper, we explore the possibility of using feature hashing as a method for learning new representations of data for fair classification. Using Difference of Equal Odds as our metric to measure fairness, we observe that using feature hashing on the Adult Dataset leads to 5.4 x improvement in metric score while losing an accuracy of 6.1% compared to when the data is used as is.