  Across various domains--such as health, education, and housing--improving
societal welfare involves allocating resources, setting policies, targeting
interventions, and regulating activities. These solutions have an immense
impact on the day-to-day lives of individuals, whether in the form of access to
quality healthcare, labor market outcomes, or how votes are accounted for in a
democratic society. Problems that can have an out-sized impact on individuals
whose opportunities have historically been limited often pose conceptual and
technical challenges, requiring insights from many disciplines. Conversely, the
lack of interdisciplinary approach can leave these urgent needs unaddressed and
can even exacerbate underlying socioeconomic inequalities. To realize the
opportunities in these domains, we need to correctly set objectives and reason
about human behavior and actions. Doing so requires a deep grounding in the
field of interest and collaboration with domain experts who understand the
societal implications and feasibility of proposed solutions. These insights can
play an instrumental role in proposing algorithmically-informed policies.
  In this article, we describe the Mechanism Design for Social Good (MD4SG)
research agenda, which involves using insights from algorithms, optimization,
and mechanism design to improve access to opportunity. The MD4SG research
community takes an interdisciplinary, multi-stakeholder approach to improve
societal welfare. We discuss three exciting research avenues within MD4SG
related to improving access to opportunity in the developing world, labor
markets and discrimination, and housing. For each of these, we showcase ongoing
work, underline new directions, and discuss potential for implementing existing
work in practice.

Computer Science and Game Theory;Artificial Intelligence;Computers and Society;Data Structures and Algorithms