Citation-based metrics dominate academic evaluation in scientometrics but often favor researchers from well-represented regions, reinforcing systemic biases in scientific recognition. To address this challenge, we introduce a fairness-aware influence maximization framework for scientometrics, identifying top-k influential authors in citation networks by considering both the number of distinct citing authors and the geographic diversity of these authors. We introduce a multi-task learning model, Fair2Cite, that jointly learns embeddings of influencers and followers, accurately estimating influence probabilities while capturing latent behavioral traits. These embeddings help in the construction of a bipartite graph, enabling the selection of fair and impactful influencers via the Independent Cascade model. Experiments on scientometric dataset demonstrate that our method outperforms state-of-the-art baselines in influence spread and achieves a more balanced citation distribution across global regions. Our framework promotes an inclusive academic ecosystem by recognizing diverse contributions beyond traditional metrics.