Today's data-driven systems and official statistics often oversimplify the concept of gender, reducing it to binary data, which carries far-reaching implications for policy development and equitable access to services. This simplification tends to result in misclassification and discrimination against individuals who identify as gender-nonconforming. The proposed research aims to develop new, more equitable approaches that can effectively circumvent discrimination based on gender identity. Within this research framework, the primary emphasis lies in addressing the problem of underrepresentation and, in some instances, the complete absence of gender-nonconforming individuals in data collection efforts. The work presented herein represents an initial endeavor to design an equitable neural network capable of accurately identifying gender within a multiclass context, including individuals whose gender identity transcends the binary spectrum.