With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct our work towards addressing complex societal problems. In pursuing this research agenda of AI for Social Impact, we present algorithmic advances as well as deployments that address one key cross-cutting challenge: how to effectively deploy our limited intervention resources within these critical problem domains. In this talk, we focus on our on-going work in public health preventative interventions. I will present results from real-world pilot deployments, initial investigations of algorithmic approaches for addressing challenges in fairness, as well as a key question for future investigation: to understand the interaction between domain-specific stakeholder perspectives on fairness and algorithmic approaches.