Empowering Local Communities Using Artificial Intelligence
Delft University of Technology（代尔夫特理工大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Carnegie Mellon University（卡内基梅隆大学）；Amsterdam Institute for Advanced Metropolitan Solutions（阿姆斯特丹高级城市解决方案研究所）
Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition. We are specifically interested in using AI-powered systems to engage local communities in developing plans or solutions for pressing societal and environmental concerns. Such local contexts often involve multiple stakeholders with different and even contradictory agendas, resulting in mismatched expectations of these systems' behaviors and desired outcomes. There is a need to investigate if AI models and pipelines can work as expected in different contexts through co-creation and field deployment. Based on case studies in co-creating AI-powered systems with local people, we explain challenges that require more attention and provide viable paths to bridge AI research with citizen needs. We advocate for developing new collaboration approaches and mindsets that are needed to co-create AI-powered systems in multi-stakeholder contexts to address local concerns.
Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review
Purple Mountain Laboratories（紫金山实验室）；Northwestern Polytechnical University（西北工业大学）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Carleton University（卡尔顿大学）
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Midea Group（美的集团）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peng Cheng Laboratory（鹏城实验室）；Terminus Group（特斯联集团）；The University of Waterloo（滑铁卢大学）
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
School of Science and Engineering（科学与工程学院）；Shenzhen Future Network of Intelligence Institute（深圳未来网络智能研究院）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Department of Information Science and Engineering（信息科学与工程系）；KTH Royal Institute of Technology（皇家理工学院）；Department of Electrical and Electronic Engineering（电子与电气工程系）；University of Hong Kong（香港大学）；Department of Electrical and Computer Engineering（电子与计算机工程系）
Empowering Polymeric Materials Discovery by Artificial Intelligence
Suzhou MatSource Technology Co., Ltd.（苏州MatSource科技有限公司）；Gusu Laboratory of Materials（材料Gusu实验室）；Advanced Institute for Materials Research (WPI-AIMR)（先进材料研究所（WPI-AIMR））；Frontier Research Institute for Interdisciplinary Sciences (FRIS)（交叉学科前沿研究所（FRIS））；State Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China（先进技术国家实验室，环境科学与工程系，中国科学技术大学）；Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Centre of Biomedical Functional Materials, School of Chemistry and Materials Science, Nanjing Normal University（新型动力电池江苏省重点实验室，生物医学功能材料协同创新中心，化学与材料科学学院，南京师范大学）；Department of Chemistry, National University of Singapore（新加坡国立大学化学系）；Thrust of Sustainable Energy and Environment, The Hong Kong University of Science and Technology (Guangzhou)（可持续能源与环境方向，香港科技大学（广州））；Department of Materials Design and Innovation, University at Buffalo（材料设计与创新系，布法罗大学）；College of Smart Materials and Future Energy, State Key Laboratory of Molecular Engineering of Polymers, Fudan University（智能材料与未来能源学院，聚合物分子工程国家重点实验室，复旦大学）；School of Physical Science and Technology, Shanghai tech University（物理科学与技术学院，上海科技大学）；The State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science, Fudan University（聚合物分子工程国家重点实验室和大分子科学系，复旦大学）；Department of Chemistry and Materials Science, Xi'an J Liverpool University（化学与材料科学系，西安J Liverpool大学）
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
Nokia Bell Labs（诺基亚贝尔实验室）；IBM Research AI（IBM研究院人工智能部门）；University of Glasgow（格拉斯哥大学）；Microsoft Research（微软研究院）；Northeastern University（东北大学）；Google（谷歌）；Meta；University of Edinburgh（爱丁堡大学）
MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture（北京建筑大学建筑与城市规划学院）
Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management
值得信赖的人工智能（AI）已经成为一个重要的话题，因为对AI系统及其创造者的信任已经丧失。研究人员、企业和政府长期以来都有将边缘化群体排除在技术开发、部署和监督之外的痛苦历史。因此，这些技术对少数群体用处不大，甚至有害。我们认为，任何渴望信任的人工智能开发、部署和监控框架都必须结合女权主义、非剥削性的参与式设计原则，以及强大的、外部的、持续的监控和测试。我们还解释了在透明度、公平性和问责制之外考虑可信度的重要性，特别是考虑正义和将权力转移给被剥夺权力的人，作为任何值得信赖的人工智能系统的核心价值。创建值得信赖的人工智能首先要资助、支持和授权像Queer in AI这样的草根组织，以便人工智能领域具有多样性和包容性，从而可信且有效地开发值得信赖的人工智能。我们利用人工智能中的酷儿通过多年的工作和宣传发展起来的专家知识，讨论性别，性行为和酷儿身份的其他方面是否以及如何在数据集和人工智能系统中使用，以及如何减轻这些方面的危害。在此基础上，我们分享了一种性别化的人工智能方法，并进一步提出了一种奇怪的认识论，并分析了它可以给人工智能带来的好处。我们还讨论了如何用这种奇怪的认识论来规范人工智能，提出了制定与人工智能和性别多样性以及隐私和奇怪的数据保护相关的政策的框架。
