fairness issue in SER. While there exist works in fair SER, our work presents one of the first studies in addressing the unique joint speaker-rater (two-sided) bias, focusing on the issue of gender fairness. Our cross-reference evaluation demonstrates that the SER fair model, which merely mitigates one-sided bias introduces biases when examining from another viewpoint. Furthermore, in order to handle model stability when optimizing for these compounded speaker-rater constraints, we introduce a flexible controlled mechanism that dynamically balances the contribution of each viewpoint. Our analyses show the efficacy of our approach in achieving a fair SER that meets the dual speaker-rater gender neutrality criterion.