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

合作研究：NSF-CSIRO：RESILIENCE：危机应对中公平团队的图表示学习

• 批准号: 2303038
• 负责人: Ying Ding
• 金额: $ 29.99万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303038&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; CSIRO; RESILIENCE

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.

最近的Covid-19大流行揭示了人类的脆弱性。 在我们高度联系的世界中，传染病可以迅速转变为全球流行病。瘟疫可以改写历史，科学可以限制损害。通过跨学科研究，在科学文献中广泛研究了团队合作在科学方面的重要性，以分析广泛的科学活动的基础机制。 科学社区如何迅速组成团队以最佳地应对大流行危机？ 已经提出了人工智能（AI）模型来推荐科学合作，特别是对于那些具有互补知识或技能的人。但是与团队公平性有关的问题，尤其是如何平衡群体的公平和个人公平仍然具有挑战性。因此，开发用于推荐团队的公平AI模型对于平等和包容的工作环境至关重要。 在下一个大流行危机中，这种需求可能是关键的。该项目将开发一个决策支持系统，以加强对传染病暴发的美洲公共卫生反应。 该系统将有助于迅速组建全球科学团队，并通过公平的组合解决方案来控制传染病，诊断和治疗。该项目将包括代表性不足的团体（澳大利亚土著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社区如何应对历史大流行危机以及如何在将来做出最好的反应，以为新的传染病危机提供公平的团队解决方案。该项目将开发一组图表表示方法，用于通过以下方面的危机响应中的公平团队推荐：1）生物医学知识图形构建和学习，以及用于新兴的生物实质提取，关系发现的新型模型，以及用于敏感人口统计学属性的公平图表； 2）对公平性的认可和团队成功的决定因素，具有基于对比度的基于学习的预测模型，用于识别核心团队单位并考虑公平与团队绩效之间的权衡； 3）学习公平推荐，通过测量基于图的最大平均差异，一种用于公平图表学习的元学习方法以及一种基于强化的学习搜索方法，用于公平团队建议。该项目将通过有效地弥合负责人AI和团队科学，公平项目管理和科学风险管理中的差距来支持跨学科课程的开发。这是美国和澳大利亚的研究人员之间的一个联合项目，并由美国NSF和澳大利亚联邦科学和工业研究组织（CSIRO）领导下的负责和公平AI的合作机会资助。这项奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是值得的支持。

项目成果

The impact of heterogeneous shared leadership in scientific teams

科学团队中异质共享领导的影响

• DOI: 10.1016/j.ipm.2023.103542
• 发表时间: 2024
• 期刊: Information Processing & Management
• 影响因子: 8.6
• 作者: Xu, Huimin;Liu, Meijun;Bu, Yi;Sun, Shujing;Zhang, Yi;Zhang, Chenwei;Acuna, Daniel E.;Gray, Steven;Meyer, Eric;Ding, Ying
• 通讯作者: Ding, Ying