A crucial pillar of research at CRCS is to advance societal fairness and quity. AI systems can perpetuate unfair outcomes and increase inequity. At CRCS, we design foundational AI systems to prevent the disparate impact of algorithms. Moreover, well-designed AI systems can help further societal equity and fairness. Designing fair and equitable AI systems complements CRCS's goals of enabling AI for enhanced public and clinical health and conservation interventions. To prevent disparate impact from unfair AI predictions, we augment AI-based predictions with safeguards that prevent disparate impact. For example, these can come in the form of an algorithmic recourse that can empower human users subjected to unfair decisions to act in ways that improve their outcomes. Importantly, we design fair and equitable AI systems using targeted interventions that advance conservation, public health, and clinical health goals. For example, we develop game-theoretic approaches to improve maternal health in low-resourced communities in South Asia. Our AI systems in clinical health can seamlessly incorporate human expertise to prevent unfair interventions. We design systems that carefully capture domain-specific (un)fairness to enable equitable AI-assisted decision-making. Finally, we are committed to creating new frameworks for equity throughout the AI development pipeline with a heavy emphasis on long-term societal good.