Abstract: Public models have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. However, their exclusive emphasis on prediction accuracy may not align with the diverse end objectives of downstream agents when utilized. Recognizing the public model's predictions as a service, we advocate for integrating the objectives of downstream agents into the optimization process. Concretely, to address performance disparities and foster fairness among heterogeneous agents in training, we propose a novel $q$-Equitable objective. This objective, coupled with a policy gradient algorithm, is crafted to train the public model to produce a more equitable/uniform performance distribution across downstream agents, each with their unique concerns.Both theoretical analysis and empirical case studies have proven the effectiveness of our method in advancing performance equity across diverse downstream agents utilizing the public model for their decision-making.