ROUND TABLE ON HUMAN-CENTERED ARTIFICIAL INTELLIGENCE
- Topic : ROUND TABLE ON HUMAN-CENTERED ARTIFICIAL INTELLIGENCE
- Date: Tuesday, October 26th, 2021
- Time: 3:00 pm - 5:30 pm
- Venue: Zoom Webinar (Registration required)
The following event information is modified based on International Research Centre in Artificial Intelligence under the auspices of UNESCO (IRCAI), please find the original and complete detail at its source: https://ircai.org/webinar-series-science-blast/
Registration: Zoom Meeting Registration (pre-set to Taipei Standard Time GMT+8)
Human-Centered AI (HCAI) is an emerging discipline that aims to create AI systems that amplify and augment human abilities and preserve human control in order to make AI partnerships more productive, enjoyable, and fair. Our workshop aims to bring together researchers and practitioners from the IRCAI and NTU communities and others with convergent interests in HCAI. With an emphasis on internationality, diversity and discussion, we will explore research questions that stem from the increasingly wide-spread usage of machine learning algorithms across all areas of society, with a specific focus on understanding both technical and design requirements for HCAI systems, as well as how to evaluate the efficacy and effects of HCAI systems. This workshop is intended to be a working group to co-construct new and combined perspectives and potentially bring forward the idea of introducing HCAI into the new IRCAI Network of Centers of Excellence in AI. We will therefore allocate much of the time for short presentations.
- Help build trust in AI as a technology
- Promote transparency in AI
- Create positive/enthusiasm for AI
- Create a report to be uploaded into the IRCAI library
AGENDA (in Taipei Standard Time GMT+8)
15:05 – 15:15 – Introduction and Welcome
Opening and Introduction
- Jane Yung-jen Hsu
- John Shawe-Taylor
Overview of the field and current topics in Human-Centered AI (HCAI)
- Over-Arching Challenges
- Areas of Action
15:15 – 16:15 – Panel 1 Roundtable on Technology towards Human-Centered AI
There is a strong consensus that AI will beget changes far more profound than any other technological revolution in human history. Depending on the course that this revolution takes, AI will either empower our ability to make more informed choices or reduce human autonomy; expand the human experience or replace it; create new forms of human activity or make existing jobs redundant; help distribute well-being for many or increase the concentration of power and wealth in the hands of a few; expand or endanger democracy in our societies.
Key topics to be discussed:
- Key advances in AI technology
- Impacts on humans/society
- Data-driven vs. human-centered
- Improved technology?
Presentations on Technical Perspectives:
- Min Sun
- Hung-yi Lee
- Nuria Oliver
- Jan Hajic
16:30 – 17:30 – Panel 2 Roundtable on Human-Centered AI Interaction
A key challenge is that HCAI solutions cannot be found by working within the traditional AI silos, but instead require breakthroughs at the interfaces of various areas of AI, HCI, cognitive science, social science, complex systems, etc. Thus, we need to bring together a unique community which has the expertise both within these silos and at the interfaces between them and can address those challenges.
Key topics to be discussed:
- Social interactions
- Ethical Issues of AI
- Impacts on society, e.g. information bubbles
- AI governance
- AI for Society 5.0
Presentations on Sociological Perspectives:
- Yueh-Hsuan Weng
- Su-Ling Yeh
- Yvonne Rogers
- Marija Slavkovik
- Min Sun received his M.S. from Stanford University and Ph.D. from the University of Michigan at Ann Arbor in 2007 and 2012, respectively. He is an expert in Computer Vision and Machine Learning (especially Deep Learning). his research interests include 3D object recognition, human pose estimation, scene understanding, video understanding, text summarization, reinforcement learning, and neural architecture search. Through research breakthroughs, his ultimate goal is to build game-changing applications to be used in our daily life. Dr. Sun also holds 4 U.S. patents and has published 50+ top AI conference papers. He won the Microsoft Research Asia Collaborative Grant in 2016, and the Digital Drift Best Paper on Deep Learning for Visual Analysis in 2016, CVGIP Best Paper Awards from 2015-2017, the Outstanding Research Award from MOST Taiwan in 2018, and Ta-You Wu Memorial Award From MOST Taiwan in 2018.
- Hung-yi Lee received the M.S. and Ph.D. degrees from National Taiwan University (NTU), Taipei, Taiwan, in 2010 and 2012, respectively. From September 2012 to August 2013, he was a postdoctoral fellow in Research Center for Information Technology Innovation, Academia Sinica. From September 2013 to July 2014, he was a visiting scientist at the Spoken Language Systems Group of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
- Su-Ling Yeh received her B.S. and M.S. degrees in Psychology from National Taiwan University (NTU), Taiwan, and Ph.D. degree in cognitive psychology from the University of California, Berkeley, USA. Since 1994, she has been with the Department of Psychology, NTU and was awarded Lifetime Distinguished Professorship in 2012. She is a recipient of Academic award of Ministry of Education and Distinguished Research Award of National Science Council of Taiwan. She is an APS (American Psychological Science) fellow, and 2019-20 Stanford-Taiwan Social Science Fellow at the Center for Advanced Study in the Behavioral Sciences, Stanford University. She serves as associate director of NTU Center for Artificial Intelligence and Advanced Robotics. Her research interests include cognitive neuroscience, perception, attention, consciousness, multisensory integration, aging, and applied research on display technology, eye tracking device, affective computing, and AI/robots.
- Yueh-Hsuan Weng is an Assistant Professor at FRIS, Tohoku University and a Visiting Scientist at RIKEN-AIP in Tokyo. He has been appointed as Tohoku University Prominent Research Fellow (2021), Stanford Law School’s TTLF Fellow (2018-2021), Visiting Assistant Professor at The University of Hong Kong (2018), Peking University’s Yahoo! Research Fellow (2010-2014). He received his Ph.D. in Law from Peking University and his M.S. in Computer Science from National (Yang Ming) Chiao Tung University. He is strongly interested in interdisciplinary studies, especially in issues concerning the interface between Artificial Intelligence and Law, including Robot Law, Social Robotics, and Legal Informatics. During his Ph.D. studies, he has founded ROBOLAW.ASIA and CHINA-LII, which are China’s first initiatives in AI Law and Free Access to Law. Currently he is also an Associate Editor of Delphi – Interdisciplinary Review of Emerging Technologies (Berlin: Lexxion Publisher).
- Jane Yung-jen Hsu is the Pegatron Professor of Computer Science and Information Engineering at National Taiwan University and Director of NTU IoX Center. Her research interests include multiagent systems, machine learning, crowdsourcing, commonsense knowledge mining, and smart IoT. Prof. Hsu is leading research collaboration with Intel Labs, Delta Research Center, and Compal, exploring topics in Augmented Collective Beings to facilitate human-AI/IoT collaboration and Digital Closeness. She has been actively involved in AAAI, TAAI, IEEE, and ACM conferences, and served as the President of the Taiwanese Association for Artificial Intelligence in 2013-14. She received the MSRA Collaborative Research Award and Intel Labs Distinguished Collaborator Award.
- John Shawe-Taylor has contributed to a number of fields ranging from mathematics of graph theory through cryptography to statistical learning theory and its applications. In graph theory central contributions were to the classification of cubic distance regular graphs, while in cryptography his RSA prime generation algorithm was incorporated into an international standard. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines. His work in this area has progressed on several parallel fronts: the refinement of the fundamental statistical results that underpin the approach and can be extended to related algorithms and data analysis techniques; the mapping of these applications onto novel domains including work in computer vision, document classification and brain scan analysis; and the extension of learning to improving the representations that are created for learning on different application domains. He has also been instrumental in assembling a series of influential European Networks of Excellence (initially the NeuroCOLT projects and later the PASCAL and PASCAL2 networks). The coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. He has also coordinated two influential European research projects, the KerMIT project, the CompLACS (Composing Learning for Artificial Cognitive Systems) and most recently the X5gon project developing AI tools for enhancing access to Open Educational Resources.
- Marija Slavkovik is a professor at the University of Bergen in Norway. Her area of research is Artificial Intelligence (AI) with expertise in collective reasoning. Slavkovik is active in the AI subdisciplines of: multi-agent systems, machine ethics and computational social choice. Slavkovik believes that the world can be improved by automating away the boring, repetitive and dangerous human tasks and that AI has a crucial role to play towards this goal. In AI, the big problem she hopes to solve is the efficient self-coordination of systems of artificial intelligent agents. In machine ethics, Slavkovik is active in engineering machine ethics problems – How can we build autonomous systems and artificial agents that behave ethically? Want to know what is happening in machine ethics since it stopped being an SF-only topic? There is a tutorial for that. Slavkovik co-organised a Dagstuhl Seminar in 2019 on this topic. She is also one of the guest editors of the Special Issue on Ethics for Autonomous Systems of the AI Journal.
- Yvonne Rogers, Chair of Interaction Design, Dept of Computer Science, Faculty of Engineering Science, UCL. Her research interests are in the areas of ubiquitous computing, interaction design and human-computer interaction. A central theme is how to design interactive technologies that can enhance life by augmenting and extending every day, learning and work activities. This involves informing, building and evaluating novel user experiences through creating and assembling a diversity of pervasive technologies.
- Nuria Oliver is a computer scientist. She holds a Ph.D. from the Media Lab at MIT. She is the first female computer scientist in Spain to be named an ACM Distinguished Scientist and an ACM Fellow. She is also a Fellow of the European Association of Artificial Intelligence and a IEEE Fellow. She is a member of the Academia Europaea and the fourth and youngest female member of the Spanish Royal Academy of Engineering. In 2018 she was named Engineer of the Year by the Professional Association of Telecommunication Engineers of Spain and she received an honorary doctorate from the University Miguel Hernandez.
- Jan Hajič is a professor of Computational Linguistics and the director of Large Research Infrastructure LINDAT/CLARIAH-CZ at Charles University, Prague, since 2010. His interests are in machine translation and creating large language resources, and in ways to make them available to the research community as well as to the commercial innovation scene. He has experience with both commercial research (IBM Research) and academic environments (Johns Hopkins University, University of Colorado in Boulder, Norway Academy of Sciences, Charles University); he has published over 200 papers with more than 11 thousand citations (h-index 46), and is listed among the world’s most influential computer scientists by research.com in 2021. He has also experience as a (Co-)PI of many EU- and US-funded projects, including the European Language Grid and European Language Equality; he is the Chair of META-NET, the Multilingual Europe Technology Alliance Network, member of the LIND (Language INDustry) High Level Expert Group of the EC, and of the EOSC Task Force on FAIR metrics, among others.