SIGKDD的使命是为知识发现和数据挖掘的“科学”的发展、教育和采用提供首要的论坛,从计算机和计算机网络中存储的所有类型的数据中进行数据挖掘。SIGKDD促进了KDD的基础研究和开发,在术语、评估、方法和KDD研究人员、从业者和用户之间采用市场上的“标准”。 官网地址:http://dblp.uni-trier.de/db/conf/kdd/

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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 1st Data-driven Humanitarian Mapping workshop at the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, August 24th, 2020.

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