The proceedings of the 2nd Data-driven Humanitarian Mapping workshop at the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 15th, 2021 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 20211 . 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.
翻译:2021年8月15日 2021年8月15日 人道主义挑战,包括自然灾害、粮食无保障、气候变化、种族和性别暴力、环境危机、COVID-19 Corona病毒流行病、侵犯人权和强迫流离失所,对全世界弱势社区的影响特别大。据人道协调厅称,2021年将有2.35亿人需要人道主义援助。尽管这些危险不断增加,但数据科学研究仍然明显缺乏,无法从科学角度为改善高危人口生计的公平公共政策决策提供科学依据。数据科学工作是应对这些挑战的,但与缺乏隐私、公平性、可解释性、问责制、透明度和道德等做法和易于受到的算法伤害的情况仍然相隔绝。数据驱动的方法有扩大影响数百万人民生计的高层决策决策中的不平等的风险。因此,决策者、从业者以及处于人道主义行动和全球发展核心的边缘化社区仍然无法从数据驱动的创新中获益。为了帮助发展高水平的公共政策规划,我们提出了利用高水平的数据模型。