Chinese CHI 2021 人机交互会议开场与闭幕演讲简介
本届Chinese CHI会议将于2021年10月16日至17日在线上举办，并免费开放同步直播。 会议获学术与产业界合作伙伴多方支持，并由来自国际知名院校及企业的资深学者担任主讲人及研讨会嘉宾。
本次大会特邀来自密歇根大学的Silvia Lindtner(林云雅)教授作为开场演讲嘉宾。
Silvia Lindtner
密歇根大学
Lindtner教授是密歇根大学伦理社会与计算中心副主任。她长久以来将中国正在经历的科技与创新产业升级及其背后深刻的社会变革作为研究的重点，通过十多年的多点民族志研究，探索中国在技术生产、经济发展和科技政策等领域的国际政治地位转变。 其著作 Prototype Nation: China and the Contested Promise of Innovation 因其创新的分析角度，扎实的研究数据与深刻的社会洞察对于相关话题的国际讨论产生了广泛而积极的影响。
Lindtner教授在她的一系列研究中提出坚持创意国家，创意城市，创意平台，创意人民的发展理念，并以此作为建设中国信息社会，增强国家软实力，与丰富社会文化生活的有效手段。 此次演讲，Lindtner女士将探讨技术创新前景在当代国家治理以及中美地缘政治中所起到的作用，并通过”创业工厂“的概念来阐释创新行业当下仍然存在的挑战与风险。 详细信息请见以下英文简介。
Title:
The Entrepreneurial Factory: Labor, Race, and Colonial Durabilities in Technology Innovation
Brief Intro:
Drawing from over ten years of ethnographic research, Dr. Lindtner will discuss the role the promise of technological innovation plays in contemporary governance and shifting geopolitics in and between China and the US. She will introduce the concept of the entrepreneurial factory to demonstrate how the kind of labor exploitation that is common in the innovation industry has become adopted by and normalized in other industries from manufacturing to agricultural production in the name of progress, the good life, happiness, and future making. Centering the experience of Chinese workers in the manufacturing and innovation industries and the racialized and gendered forms of violence they experience, this talk demonstrates that by attending to bodies, labor, and colonial pasts that are necessary to sustain the entrepreneurial factory, technology can be extricated from the seemingly endless cycle of promise and violence.
About Silvia Lindtner
Silvia Lindtner (she/her) is Associate Professor at the University of Michigan in the School of Information and Associate Director of the Center for Ethics, Society, and Computing (ESC). Lindtner draws from more than ten years of multi-sited ethnographic research, exploring China's shifting position in the global political economy of technology production, economic development, and science and technology policy. She is the author of Prototype Nation: China and the Contested Promise of Innovation (Princeton University Press, 2020). The book reveals how a growing distrust in Western models of progress and development, including Silicon Valley and the tech industry after the financial crisis of 2007–8, shaped the vision of China as a “new frontier” of innovation. Lindtner’s work contributes to the fields of STS (science and technology studies), cultural and feminist anthropology, China studies, HCI (human computer interaction), global communication studies, science and technology policy, and design. Her research has been awarded support from the US National Science Foundation, IMLS, Intel Labs, Google Anita Borg, and the Chinese National Natural Science Foundation.
本次大会特邀闭幕演讲嘉宾为来自香港科技大学的屈华民教授。
屈华民
香港科技大学
屈华民教授担任香港科技大学计算机科学与工程系的讲座教授，可视化实验室主任，并兼任跨学科课程事务处主任。作为数据可视化与人机交互领域的学术领军人，屈教授的研究重点关注城市信息学、社交网络分析、数字化学习、文本可视化以及可解释人工智能(XAI)等， 并在这些方向取得了丰硕的成果。 屈华民教授参与写作并发表的200多篇学术论文中，有80多篇被收录于 IEEE Transactions on Visualization and Computer Graphics (TVCG)和 ACM CHI等重要期刊和会议，同时他的研究也获得多个学术奖项的认可，其中包括14个最佳论文奖项和提名， AI 2000 Most Influential Scholar Award, IBM Faculty Award, 华为诺亚方舟实验室的Distinguished Collaborator Award等。
此次演讲，屈华民教授将探讨大数据时代 human-powered AI的应用案例（包括供应链管理、电子学习和医疗保健等方面），为我们讲解在人工智能领域中视觉化分析如何作用于不同类型的可解释性(explainability)， 并介绍一些针对具体人工智能技术进行的视觉化分析技术开发。 详细信息请见以下英文简介。
Title:
VIS+AI: A Visual Analytics Approach for Human-AI Collaboration
Brief Intro:
We live in the era of big data and AI. Data is everywhere and decision-making has become more and more data-driven. With tremendous amounts of data, massive computational power, and advances in machine learning algorithms, especially deep learning, AI has become very powerful and makes decisions for us every day. However, issues with model transparency and interpretability limit the adoption of AI in domains like healthcare, security, and finance. Explainable Artificial Intelligence (XAI) tries to make AI systems and their actions more understandable and transparent to humans. Visual analytics has been playing an important role in XAI. In this talk, I will introduce how visual analytics can help with different types of explainability (e.g., local vs. global explanation, white-box vs. black -box explanation) and the visual analytics techniques developed for specific AI techniques like Random Forest, CNN, RNN, LSTM, GNN, and Automated Machine Learning (AML). Case studies with human-powered AI in different application domains like supply chain management, E-learning, and health care will be presented. I will conclude the talk by discussing some emerging research topics for human-AI collaboration.
About Huamin Qu
Prof. Huamin Qu is a chair professor in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST) and is also the director of the interdisciplinary programs office (IPO) of HKUST. He obtained his PhD in Computer Science from Stony Brook University. He is the director of the VisLab at HKUST and serves as the coordinator of the Human-Computer Interaction (HCI) group. His main research interests are in visualization and human-computer interaction, with focuses on urban informatics, social network analysis, E-learning, text visualization, and explainable artificial intelligence (XAI). He has co-authored about 200 refereed papers and more than 80 papers are published in the IEEE Transactions on Visualization and Computer Graphics (TVCG) and ACM CHI. His research has been recognized by many awards including 14 best paper/honorable mention awards, AI 2000 Most Influential Scholar Award, IBM Faculty Award, and Distinguished Collaborator Award from Huawei Noah's Ark Lab. In 2020, Huamin was inducted into the IEEE VIS Visualization Academy which highlights the significant accomplishments of leaders in the data visualization field.