  Humanitarian challenges, including natural disasters, food insecurity,
climate change, racial and gender violence, environmental crises, the COVID-19
coronavirus pandemic, human rights violations, and forced displacements,
disproportionately impact vulnerable communities worldwide. According to UN
OCHA, 235 million people will require humanitarian assistance in 2021. Despite
these growing perils, there remains a notable paucity of data science research
to scientifically inform equitable public policy decisions for improving the
livelihood of at-risk populations. Scattered data science efforts exist to
address these challenges, but they remain isolated from practice and prone to
algorithmic harms concerning lack of privacy, fairness, interpretability,
accountability, transparency, and ethics. Biases in data-driven methods carry
the risk of amplifying inequalities in high-stakes policy decisions that impact
the livelihood of millions of people. Consequently, proclaimed benefits of
data-driven innovations remain inaccessible to policymakers, practitioners, and
marginalized communities at the core of humanitarian actions and global
development. To help fill this gap, we propose the Data-driven Humanitarian
Mapping Research Program, which focuses on developing novel data science
methodologies that harness human-machine intelligence for high-stakes public
policy and resilience planning.
  The proceedings of the 2nd Data-driven Humanitarian Mapping workshop at the
27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 15th,
2021

Computers and Society;Artificial Intelligence;General Economics;Economics