  Public models offer predictions to a variety of downstream tasks and have
played a crucial role in various AI applications, showcasing their proficiency
in accurate predictions. However, the exclusive emphasis on prediction accuracy
may not align with the diverse end objectives of downstream agents. 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 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. Codes and datasets are released at
https://github.com/Ren-Research/Socially-Equitable-Public-Models.
