Proceedings of NeurIPS 2019 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response

Proceedings of NeurIPS 2020 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response

These are the "proceedings" of the 2nd AI + HADR workshop which was held virtually on December 12, 2020 as part of the Neural Information Processing Systems conference. These are non-archival and merely serve as a way to collate all the papers accepted to the workshop.

轻量级LLM框架用于灾难人道主义信息分类

A Lightweight LLM Framework for Disaster Humanitarian Information Classification

本文提出轻量级LLM框架，通过参数高效微调实现灾难推文的人道主义信息分类和事件类型识别，展示LoRA和QLoRA在资源受限环境下的高效性能。

可验证的灾难故事情节与因果知识图谱：基于引文的异构人道主义来源处理流程

Verifiable Disaster Storylines and Causal Knowledge Graphs: A Citation-Grounded Pipeline from Heterogeneous Humanitarian Sources

该研究提出结合EM-DAT等异构数据源的检索增强生成流程，生成可溯源的灾难故事情节与因果知识图谱，经评估其检索精度高、因果关系忠实，获专家偏好，拟扩展至全EM-DAT目录并公开叙事增强版本。

Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning

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…

Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning

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 …

评估面向无障碍灾害援助的跨文本与音频模态多模态大语言模型

Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance

本文评估了开放权重多模态大语言模型在灾害援助场景下跨文本与音频模态的响应一致性，发现其存在模态依赖的不公平性，为构建公平的灾害沟通AI工具提供了设计建议。