Collaborative Research: NSF-CSIRO: RESILIENCE: Graph Representation Learning for Fair Teaming in Crisis Response

最近的COVID-19大流行揭示了人类的脆弱性。 在我们这个高度互联的世界中，传染病可以迅速转变为全球性流行病。瘟疫可以改写历史，科学可以限制破坏。团队合作在科学中的重要性已经在科学学文献中得到了广泛的研究，使用跨学科研究来分析广泛的科学活动的机制。 科学界如何迅速组建团队，以最佳方式应对流行病危机？ 人工智能（AI）模型已经被提出来推荐科学合作，特别是对于那些具有互补知识或技能的人。但团队公平性的相关问题，尤其是如何平衡团队公平性和个人公平性仍然具有挑战性。因此，开发公平的人工智能模型来推荐团队对于平等和包容的工作环境至关重要。 这种需要在下一次流行病危机中可能是关键的。该项目将开发一个决策支持系统，以加强美国-澳大利亚对传染病爆发的公共卫生反应。 该系统将有助于快速组建全球科学团队，为传染病控制、诊断和治疗提供公平的团队解决方案。该项目将包括代表性不足的群体（土著澳大利亚人和西班牙裔美国人）的参与，并将在广泛的工作和招聘场景中提供公平的团队解决方案。该项目旨在了解科学界如何应对历史上的大流行危机，以及如何在未来最好地应对新的传染病危机，为新的传染病危机提供公平的团队解决方案。该项目将开发一套图表示学习方法，用于危机响应中的公平团队推荐：1）生物医学知识图构建和学习，具有新兴生物实体提取，关系发现和敏感人口统计属性的公平图表示学习的新模型;（2）对公平的认识和团队成功的决定因素;基于子图对比学习的预测模型，用于识别核心团队单元并考虑交易，公平性和团队绩效之间的差异;以及3）学习公平地推荐，使用基于图的最大平均差异的测量，用于公平图表示学习的Meta学习方法，以及用于公平团队推荐的基于强化学习的搜索方法。该项目将通过有效弥合负责任的人工智能和团队科学、公平的项目管理和科学风险管理方面的差距，支持跨学科课程开发。这是美国和澳大利亚的研究人员之间的联合项目，由美国NSF和澳大利亚联邦科学与工业研究组织（CSIRO）的负责任和公平AI合作机会资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The recent COVID-19 pandemic has revealed the fragility of humankind. In our highly connected world, infectious disease can swiftly transform into worldwide epidemics. A plague can rewrite history and science can limit the damage. The significance of teamwork in science has been extensively studied in the science of science literature using transdisciplinary studies to analyze the mechanisms underlying broad scientific activities. How can scientific communities rapidly form teams to best respond to pandemic crises? Artificial intelligence (AI) models have been proposed to recommend scientific collaboration, especially for those with complementary knowledge or skills. But issues related to fairness in teaming, especially how to balance group fairness and individual fairness remain challenging. Thus, developing fair AI models for recommending teams is critical for an equal and inclusive working environment. Such a need could be pivotal in the next pandemic crisis. This project will develop a decision support system to strengthen the US-Australia public health response to infectious disease outbreak. The system will help to rapidly form global scientific teams with fair teaming solutions for infectious disease control, diagnosis, and treatment. The project will include participation of underrepresented groups (Indigenous Australians and Hispanic Americans) and will provide fair teaming solutions in broad working and recruiting scenarios. This project aims to understand how scientific communities have responded to historical pandemic crises and how to best respond in the future to provide fair teaming solutions for new infectious disease crises. The project will develop a set of graph representation learning methods for fair teaming recommendation in crisis response through: 1) biomedical knowledge graph construction and learning, with novel models for emerging bio-entity extraction, relationship discovery, and fair graph representation learning for sensitive demographical attributes; 2) the recognition of fairness and the determinant of team success, with a subgraph contrastive learning-based prediction model for identifying core team units and considering trade-offs between fairness and team performance; and 3) learning to recommend fairly, with a measurement of graph-based maximum mean discrepancy, a meta learning method for fair graph representation learning, and a reinforcement learning-based search method for fair teaming recommendation. The project will support cross-disciplinary curriculum development by effectively bridging gaps in responsible AI and team science, fair project management, and risk management in science. This is a joint project between researchers from the United States and Australia and funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.