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大流行暴露了人类的脆弱性。 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流行病。瘟疫可以改写历史，科学可以限制损失。科学中团队合作的重要性已在科学文献中得到广泛研究，利用跨学科研究来分析广泛科学活动背后的机制。 科学界如何快速组建团队以最好地应对大流行危机？ 人工智能（AI）模型已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。但与团队公平相关的问题，特别是如何平衡群体公平和个人公平仍然具有挑战性。因此，为推荐团队开发公平的人工智能模型对于平等和包容的工作环境至关重要。 这种需求可能在下一次大流行危机中至关重要。该项目将开发一个决策支持系统，以加强美国和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（澳大利亚原住民和西班牙裔美国人）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，以及未来如何最好地应对，为新的传染病危机提供公平的合作解决方案。该项目将通过以下方式开发一套用于危机应对中公平团队推荐的图表示学习方法：1）生物医学知识图构建和学习，采用新兴生物实体提取、关系发现和敏感人口统计的公平图表示学习的新模型属性; 2）认识到公平性和团队成功的决定因素，使用基于子图对比学习的预测模型来识别核心团队单位并考虑公平性和团队绩效之间的权衡； 3）学习公平推荐，通过基于图的最大均值差异的测量、用于公平图表示学习的元学习方法以及基于强化学习的公平分组推荐搜索方法。该项目将通过有效弥合负责任的人工智能和团队科学、公平项目管理和科学风险管理方面的差距，支持跨学科课程的开发。这是美国和澳大利亚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) 下的负责任和公平人工智能合作机会资助。该奖项反映了 NSF 的法定使命，并已被通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

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