BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ACM SIGCHI - ECPv6.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ACM SIGCHI
X-ORIGINAL-URL:https://archive.sigchi.org
X-WR-CALDESC:Events for ACM SIGCHI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210920T100000
DTEND;TZID=America/New_York:20210920T110000
DTSTAMP:20260814T052449
CREATED:20210808T193556Z
LAST-MODIFIED:20210808T193556Z
UID:10000322-1632132000-1632135600@archive.sigchi.org
SUMMARY:Seda Gürses: Responsible Use of Data
DESCRIPTION:The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This second edition is on “Responsible Use of Data“. The series features prominent researchers and practitioners\, whose work has made fundamental contributions in these fields.\n\n\n\n\n\n\n\n\nThe adoption of artificial intelligence\, data science\, data analytics\, among other techniques is predominant in many contexts and domains: often used to help us decide which items to buy\, what music to listen to\, and in high-stakes domains such as education\, healthcare provision or criminal justice\, among others. The performance of such AI systems depends both on the learning algorithms\, as well as the data used for their training and evaluation. The role of the algorithms is well studied. In contrast\, research that focuses on the data used in AI systems is not commonplace. Data\, however\, is always at their core\, being a crucial component for advancing and assessing the technological field.
URL:https://archive.sigchi.org/event/seda-gurses-responsible-use-of-data-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210726T100000
DTEND;TZID=America/New_York:20210726T120000
DTSTAMP:20260814T052449
CREATED:20210602T110221Z
LAST-MODIFIED:20210602T110221Z
UID:10000314-1627293600-1627300800@archive.sigchi.org
SUMMARY:Krishnaram Kenthapadi - Responsible Use of Data
DESCRIPTION:The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This second edition is on “Responsible Use of Data“. The series features prominent researchers and practitioners\, whose work has made fundamental contributions in these fields.\n\nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/krishnaram-kenthapadi-responsible-use-of-data/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210712T100000
DTEND;TZID=America/New_York:20210712T120000
DTSTAMP:20260814T052449
CREATED:20210602T110004Z
LAST-MODIFIED:20210602T110004Z
UID:10000313-1626084000-1626091200@archive.sigchi.org
SUMMARY:Solon Barocas - Responsible Use of Data
DESCRIPTION:The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This second edition is on “Responsible Use of Data“. The series features prominent researchers and practitioners\, whose work has made fundamental contributions in these fields.\n\nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/solon-barocas-responsible-use-of-data/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210705T100000
DTEND;TZID=America/New_York:20210705T120000
DTSTAMP:20260814T052449
CREATED:20210602T105924Z
LAST-MODIFIED:20210602T105924Z
UID:10000312-1625479200-1625486400@archive.sigchi.org
SUMMARY:Seda Gürses - Responsible Use of Data
DESCRIPTION:The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This second edition is on “Responsible Use of Data“. The series features prominent researchers and practitioners\, whose work has made fundamental contributions in these fields.\n\nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/seda-gurses-responsible-use-of-data/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210628T100000
DTEND;TZID=America/New_York:20210628T120000
DTSTAMP:20260814T052449
CREATED:20210602T105804Z
LAST-MODIFIED:20210602T105804Z
UID:10000311-1624874400-1624881600@archive.sigchi.org
SUMMARY:Catherine D'Ignazio - Data Feminism: Ethics and Action
DESCRIPTION:Abstract: As data are increasingly mobilized in the service of governments and corporations\, their unequal conditions of production\, their asymmetrical methods of application\, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists–and others who rely on data in their work–to ignore. But it is precisely this power that makes it worth asking: “Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism\, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action\, this talk will show how challenges to the male/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never\, ever “speak for themselves.” D’Ignazio will introduce the principles of Data feminism as well as discuss recent projects that are using feminist ethics to work towards justice with computation and data science. The goal of this talk is to model how scholarship can be transformed into action and how feminist thinking can be operationalized in order to undertake more ethical and equitable data practices. \nSpeaker Biography: Catherine D’Ignazio is a scholar\, artist/designer and hacker mama who focuses on feminist technology\, data literacy and civic engagement. She has run reproductive justice hackathons\, designed global news recommendation systems\, created talking and tweeting water quality sculptures\, and led walking data visualizations to envision the future of sea level rise. With Rahul Bhargava\, she built the platform Databasic.io\, a suite of tools and activities to introduce newcomers to data science. Her 2020 book from MIT Press\, Data Feminism\, co-authored with Lauren Klein\, charts a course for more ethical and empowering data science practices. Her research at the intersection of technology\, design & social justice has been published in the Journal of Peer Production\, the Journal of Community Informatics\, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). Her art and design projects have won awards from the Tanne Foundation\, Turbulence.org and the Knight Foundation and exhibited at the Venice Biennial and the ICA Boston. D’Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT. She is also Director of the Data + Feminism Lab which uses data and computational methods to work towards gender and racial equity\, particularly in relation to space and place. \nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/catherine-dignazio-data-feminism-ethics-and-action/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210621T100000
DTEND;TZID=America/New_York:20210621T120000
DTSTAMP:20260814T052449
CREATED:20210602T105649Z
LAST-MODIFIED:20210602T105649Z
UID:10000310-1624269600-1624276800@archive.sigchi.org
SUMMARY:Elena Simperl - Citizen Science: the data view
DESCRIPTION:Abstract: Citizen science (CS) is about carrying out scientific research and engagement with the help of the public. Many popular initiatives in this space produce data for scientific experiments and involve members of the public who don’t have to be trained scientists in collecting observations in the field or analysing data to train machine learning algorithms. In this talk\, I will discuss the central role data plays in citizen science\, both as a core outcome of CS projects that needs to be curated\, documented\, shared and preserved\, and as a means to improve our understanding of citizen science. I will present challenges and opportunities drawing from studies with CS communities in astrophysics\, environmental sciences and smart mobility. \nSpeaker Biography: Elena Simperl is professor of computer science at King’s College London\, a Fellow of the British Computer Society and former Turing fellow. According to AMiner\, she is in the top 100 most influential scholars in knowledge engineering of the last decade\, as well as in the Women in AI 2000 ranking. Before joining King’s College early 2020\, she held positions at the University of Southampton\, as well as in Germany and Austria. She has contributed to more than 20 research projects\, often as principal investigator or project lead. Currently\, she is the PI of two grants: H2020 ACTION\, where she develops human-AI methods to make participatory science thrive\, and EPSRC Data Stories\, where she works on frameworks and tools to make data more engaging for everyone. She authored more than 200 peer-reviewed publications in knowledge engineering\, semantic technologies\, open and linked data\, social computing\, crowdsourcing and data-driven innovation. Over the years she served as programme and general chair to several conferences\, including the European and International Semantic Web Conference\, the European Data Forum and the AAAI Conference on Human Computation and Crowdsourcing. \nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/elena-simperl-citizen-science-the-data-view/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210614T100000
DTEND;TZID=America/New_York:20210614T120000
DTSTAMP:20260814T052449
CREATED:20210602T105332Z
LAST-MODIFIED:20210602T105346Z
UID:10000309-1623664800-1623672000@archive.sigchi.org
SUMMARY:Q. Vera Liao - Questioning the AI: Towards Human-Centered Explainable AI (XAI)
DESCRIPTION:Abstract: Artificial Intelligence technologies are increasingly used to make decisions and perform autonomous tasks in critical domains such as healthcare\, finance\, and criminal justice. The needs to understand AI in order to improve\, contest\, develop appropriate trust and better interact with AI systems have spurred great academic and public interest in Explainable AI (XAI). Recently\, open-source toolkits\, including IBM Research’s AI Explainability 360\, are making a growing collection of XAI techniques into practitioners’ toolbox. My colleagues and I at IBM Research conduct human-computer interaction (HCI) research that aims to empower AI practitioners to make effective and responsible use of such a toolbox to create good XAI user experiences. Meanwhile\, our work provides insights into real-world user needs for AI explainability to inform gaps and opportunities for XAI algorithmic research. Our work follows two complementary paths. First\, we conduct HCI research by designing and studying XAI systems of various use cases in the AI lifecycle. Second\, we study AI design practices of product teams and engage with the design community to develop and advocate for user-centered design processes for XAI. I will conclude the talk with lessons learned for bridging the process of creating responsible AI systems and empowering people in the process. \nSpeaker Biography: Q. Vera Liao is a Research Staff Member in IBM T.J. Watson Research Center\, working in the “Trusted AI” area. Her research background is in human-computer interaction (HCI)\, with current focuses on human-AI interaction\, explainable AI\, and conversational agents. Her work received multiple awards at ACM CHI and IUI. She was awarded IBM Outstanding Research Accomplishments for contributions to IBM’s Watson Assistant and Trusted AI toolkits. She serves on the Editorial Board of International Journal of Human-Computer Studies (IJHCS) and ACM Transactions on Interactive Intelligent Systems (TiiS)\, and the Organizing Committee for CSCW 2021 and IUI 2019. She received a Ph.D. in Computer Science and a M.S. in Human Factors from University of Illinois at Urbana-Champaign\, and a bachelor’s degree in Industrial Engineering from Tsinghua University. \nRSVP: https://groups.google.com/g/taff-wis-tudelft \n 
URL:https://archive.sigchi.org/event/q-vera-liao-questioning-the-ai-towards-human-centered-explainable-ai-xai/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210607T100000
DTEND;TZID=America/New_York:20210607T120000
DTSTAMP:20260814T052449
CREATED:20210602T104839Z
LAST-MODIFIED:20210602T105249Z
UID:10000308-1623060000-1623067200@archive.sigchi.org
SUMMARY:Lora Aroyo - Uncovering Unknown Unknowns in Machine Learning
DESCRIPTION:Abstract: The performance of machine learning (ML) models depends both on the learning algorithms\, as well as the data used for training and evaluation. The role of the algorithms is well studied and the focus of a multitude of challenges\, such as SQuAD\, GLUE\, ImageNet\, and many others. In addition\, there have been efforts to also improve the data\, including a series of workshops addressing issues for ML evaluation. In contrast\, research and challenges that focus on the data used for evaluation of ML models are not commonplace. Furthermore\, many evaluation datasets contain items that are easy to evaluate\, e.g.\, photos with a subject that is easy to identify\, and thus they miss the natural ambiguity of real world context. The absence of ambiguous real-world examples in evaluation undermines the ability to reliably test machine learning performance\, which makes ML models prone to develop “weak spots”\, i.e.\, classes of examples that are difficult or impossible for a model to accurately evaluate\, because that class of examples is missing from the evaluation set. To address the problem of identifying these weaknesses in ML models\, we recently launched the Crowdsourcing Adverse Test Sets for Machine Learning (CATS4ML) Data Challenge at HCOMP 2020 open to researchers and developers worldwide. The goal of the challenge is to raise the bar in ML evaluation sets and to find as many examples as possible that are confusing or otherwise problematic for algorithms to process. CATS4ML relies on people’s abilities and intuition to spot new data examples about which machine learning is confident\, but actually misclassified. This first edition of the CATS4ML Data Challenge focuses on visual recognition\, using images and labels from the Open Images Dataset. The target images for the challenge are selected from the Open Images Dataset along with a set of 24 target labels from the same dataset. The challenge participants are invited to invent new and creative ways to explore this existing publicly available dataset and\, focussed on a list of pre-selected target labels\, discover examples of unknown unknowns for ML models. For more details read the blog post: https://ai.googleblog.com/2021/02/uncovering-unknown-unknowns-in-machine.html \nSpeaker Biography: Lora Aroyo is a Research Scientist at Google\, NY currently working on human-labeled data quality . She is best known for her work on CrowdTruth crowdsourcing methodology. Throughout her career\, Lora was a principal investigator of a large number of research projects bringing together methods and tools from human computation\, linked (open) data\, data science & human-computer interaction with the goal of building hybrid human-AI systems for understanding text\, images\, and videos with humans-in-the-loop. Her research projects focussing on personalized access to online multimedia have a major impact and established her as a recognized leader in human computation techniques for digital humanities\, cultural heritage\, and interactive TV. Prior to joining Google\, she worked at the VU University Amsterdam as Full Professor in Computer Science and was Chief Scientist at NY-based startup Tagasauris. She is a four times holder of IBM Faculty Award for her work on CrowdTruth used in adapting the IBM Watson system to the medical domain and in capturing ambiguity in understanding misinformation. She is currently president of the User Modeling Inc\, which acts as a steering committee for the UMAP conference series. \nRSVP: https://groups.google.com/g/taff-wis-tudelft
URL:https://archive.sigchi.org/event/lora-aroyo-uncovering-unknown-unknowns-in-machine-learning/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210506T110000
DTEND;TZID=America/New_York:20210506T120000
DTSTAMP:20260814T052449
CREATED:20210602T103847Z
LAST-MODIFIED:20210602T103847Z
UID:10000305-1620298800-1620302400@archive.sigchi.org
SUMMARY:Shadan Sadeghian - Automated Vehicles from Technology to Environmental and Social Aspects
DESCRIPTION:Abstract With increasing levels of driving automation\, the driving tasks and the interaction between humans and vehicles will change. In this conversation\, we will look briefly at three aspects of driving automation: technological\, environmental\, and social. Assuming human’s engagement in driving task until full automation is there\, raises the question of “how should in-vehicle UIs be designed to consider users’ and context’s state and result in safe and smooth maneuvers?” Besides drivers’ comfort and safety\, automated driving can have environmental effects. Given the rising problem of climate change\, it is vital to design future technologies in a way that supports sustainable mobility. And finally\, road traffic is a social situation. While the decisions related to the design of technology for automated vehicles are mostly technical\, they have social consequences. Therefore\, there is a need for a prosocial approach in the design of technology that reflects road users’ well-being and aims for considerate and supportive behavior. \nBio: Shadan Sadeghian is a Post-Doc researcher in the department of “Ubiquitous Design / Experience and Interaction” at the University of Siegen. She studied computer science at the University of Bonn and RWTH Aachen and pursued her Ph.D. in Human-Computer Interaction at OFFIS Institute for Information Technology and the University of Oldenburg. She has also worked as a Ph.D. scholar in the Max Planck Institute for Biological Cybernetics in Tübingen and as a Post-Doc researcher at Fraunhofer institute FKIE. Her research focuses on designing interaction and user experience in automated vehicles and automated systems in production management context. \nRSVP:https://unh.zoom.us/j/92719810993?pwd=a0ZoRVlkSmErVjYzbHpUQytQcnN5Zz09 \nPassword: 459641
URL:https://archive.sigchi.org/event/shadan-sadeghian-automated-vehicles-from-technology-to-environmental-and-social-aspects/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210222T100000
DTEND;TZID=America/New_York:20210222T110000
DTSTAMP:20260814T052449
CREATED:20201125T225129Z
LAST-MODIFIED:20210217T143223Z
UID:10000187-1613988000-1613991600@archive.sigchi.org
SUMMARY:Edith Law
DESCRIPTION:Speaker Biography: Edith Law is an Associate Professor at the David R. Cheriton School of Computer Science at University of Waterloo. Her research focuses on how people can enhance intelligent systems (e.g.\, human-in-the-loop systems\, crowdsourcing) as well as how people can make sense of intelligent systems\, including issues related to transparency\, engagement\, trust and collaboration. She is interested in developing technologies that leverage the AI-people partnership to tackle more complex problems in business\, science and medicine. \nShe is part of the Human Computer Interaction Lab. Her work is funded by NSERC Discovery Grant\, NSERC-CIHR Collaborative Health Research Project (CHRP) as well as the CFI-JELF program. \nPreviously\, she was a CRCS postdoctoral fellow at Harvard University. She graduated with a Ph.D. in Machine Learning from Carnegie Mellon University in 2012. \nHomepage: http://edithlaw.ca/ \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/edith-law/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210215T100000
DTEND;TZID=America/New_York:20210215T233000
DTSTAMP:20260814T052449
CREATED:20210122T175403Z
LAST-MODIFIED:20210122T175403Z
UID:10000034-1613383200-1613431800@archive.sigchi.org
SUMMARY:Olga Megorskaya
DESCRIPTION:Title: TBD \nSpeaker Biography: Olga is responsible for providing human-labeled data for all AI projects at Yandex. She is also in charge of implementing crowd-based human-in-the-loop solutions in such areas as software testing\, customer support\, product localization\, generation of content\, etc. Olga helped Yandex to grow the number of crowd performers involved in data labeling from several dozens in 2009 up to 2.2M in 2019. She graduated from the Saint Petersburg State University as a specialist in Mathematical Methods and Modeling in Economics. Also\, she was a co-author of research papers and tutorials on efficient crowdsourcing and quality control at SIGIR\, CVPR\, KDD\, WSDM\, and SIGMOD. \nHomepage: https://ru.linkedin.com/in/omegorskaya \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/olga-megorskaya/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210215T100000
DTEND;TZID=America/New_York:20210215T113000
DTSTAMP:20260814T052449
CREATED:20210122T175026Z
LAST-MODIFIED:20210122T175026Z
UID:10000219-1613383200-1613388600@archive.sigchi.org
SUMMARY:Simo Hosio
DESCRIPTION:Title: TBD \nSpeaker Biography: Simo Hosio is a social computing scientist\, with a background rooted in ubiquitous computing and human-computer interaction. He was appointed as an Associate Professor (tenure track) in the GenZ strategic profiling theme of the University of Oulu in 2020. Currently\, he focuses on crowd computing and increasingly on co-creation. Through his research\, he investigates and develop interactive means for orchestrating the inherent intelligence of large numbers of people\, i.e. crowds\, for things such as decision or creativity support\, and for digital health solutions. \nHomepage: https://simohosio.com/ \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/simo-hosio/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210201T100000
DTEND;TZID=America/New_York:20210201T113000
DTSTAMP:20260814T052449
CREATED:20210122T175212Z
LAST-MODIFIED:20210122T175212Z
UID:10000220-1612173600-1612179000@archive.sigchi.org
SUMMARY:Nithya Sambasivan
DESCRIPTION:Title: Everyone wants to do the model work\, not the data work: Human-Data Interaction in AI \nAbstract: AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact to living beings. Paradoxically\, data is the most under-valued and de-glamorized aspect of AI. In this paper\, we report on data practices in high-stakes AI\, from interviews with 53 AI practitioners in India\, East and West African countries\, and USA. We define and report on Data Cascades compounding events causing negative\, downstream effects from data issues triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence)\, invisible\, delayed\, but often avoidable. Data cascades demonstrate how broken incentives for data quality impact vulnerable groups tigers alive or dead\, cancer diagnosed or not. We also discuss findings on data collectors and raters\, paying attention to their values\, processes\, and tools. We discuss opportunities for HCI to design and incentivize data excellence\, moving from reactive to proactive focus on data work and workers in AI resulting in safer and robust systems for all. \nSpeaker Biography: Nithya Sambasivan is a Staff Researcher at PAIR and leads the HCI-AI group at Google Research India\, Bangalore. Nithya’s current research focuses on using HCI techniques in developing responsible AI in India\, with a focus on marginalized communities. Specific sub-areas are data\, fairness\, privacy and abuse\, and consent. She publishes in the fields of HCI\, ICTD\, and Privacy/Security. Nithya’s long-standing research agenda has been on HCI and under-represented communities in the Global South. She has a PhD. in Informatics from UC Irvine and a Master’s in HCI from Georgia Tech. \nHomepage: https://nithyasambasivan.com/ \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/nithya-sambasivan/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210118T100000
DTEND;TZID=America/New_York:20210118T110000
DTSTAMP:20260814T052449
CREATED:20201125T224936Z
LAST-MODIFIED:20201125T225045Z
UID:10000186-1610964000-1610967600@archive.sigchi.org
SUMMARY:Michael Bernstein
DESCRIPTION:Title: TBD \nSpeaker Biography: Michael Bernstein is an Associate Professor of Computer Science and STMicroelectronics Faculty Scholar at Stanford University\, where he is a member of the Human-Computer Interaction Group. His research focuses on the design of social computing and crowdsourcing systems. This research has won best paper awards at top conferences in human-computer interaction\, including CHI\, CSCW\, and UIST\, and recently a Lasting Impact Award from UIST 2020. Michael has been recognized with an NSF CAREER award and an Alfred P. Sloan Fellowship. He holds a bachelor’s degree in Symbolic Systems from Stanford University\, as well as a master’s degree and a Ph.D. in Computer Science from MIT. \nHomepage: https://hci.stanford.edu/msb/ \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/michael-bernstein/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210111T120000
DTEND;TZID=America/New_York:20210111T130000
DTSTAMP:20260814T052449
CREATED:20201125T224824Z
LAST-MODIFIED:20201126T141657Z
UID:10000185-1610366400-1610370000@archive.sigchi.org
SUMMARY:Panos Ipeirotis
DESCRIPTION:Speaker Biography: Panos Ipeirotis is a Professor and David Margolis Teaching Excellence Faculty Fellow at the Department of Technology\, Operations\, and Statistics at the Leonard N. Stern School of Business of New York University\, and also an associated faculty member at the Center for Data Science and Computer Science departments. \nHe is also a Distinguished Scientist at Compass\, a role assumed after Compass acquired Detectica\, a startup he co-founded with Foster Provost. \nHe has received ten “Best Paper” awards and nominations\, a CAREER award from the National Science Foundation\, and is the recipient of the 2015 Lagrange Prize in Complex Systems\, for his contributions in the field of social media\, user-generated content\, and crowdsourcing. \nHe got his Ph.D. degree in Computer Science from Columbia University in 2004. \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/panos-ipeirotis/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T100000
DTEND;TZID=America/New_York:20201214T110000
DTSTAMP:20260814T052449
CREATED:20201125T224546Z
LAST-MODIFIED:20201125T224546Z
UID:10000184-1607940000-1607943600@archive.sigchi.org
SUMMARY:Redefining Productivity to Adapt to a Changing Landscape of Work - Shamsi Iqbal
DESCRIPTION:Title: Redefining Productivity to Adapt to a Changing Landscape of Work \nAbstract: As our work environments and work practices rapidly evolves as a result of the changing landscape of work\, what we envision as the future of work is being fundamentally challenged. Research in the area of productivity and multitasking has to adapt to the changing world anticipating what the future may look like – in particular taking into account growing needs of balancing work and life. In this research I will talk about redefining productivity where doing work is no longer confined to being at a desk and the need to do things while on the go or while in divided attention scenarios continues to dominate. Our team at Microsoft Research has looked at how complex tasks can be done without having to allocate larger chunks of time\, rather\, make use of seemingly unusable ‘micromoments’ – by decomposing a task into smaller tasks that can be done in a few moments. This work brings together theories from cognitive science\, human computer interaction and artificial intelligence. I will discuss a few ongoing projects in this area and present directions for research and product development. \nSpeaker Biography: Dr. Shamsi T. Iqbal is a Principal Researcher in the Productivity and Intelligence group (P+I) in Microsoft Research\, Redmond. Her primary expertise is in the domain of Attention Management and Interruptions. More recently her work has focused on redefining productivity\, introducing novel ways of being productive through leveraging micromoments and balancing productivity and well-being in interaction design. Her work on driving and distraction has been featured in the New York Times\, MIT Tech Review among others\, and also featured in the King 5 News (NBC affiliate in the Seattle area). Shamsi has served on many organizing and program committees for Human-Computer Interaction conferences\, is currently serving as an ACM TOCHI Associate Editor and was the General Co-chair for UIST 2020. Shamsi received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2008 and Bachelors in Computer Science and Engineering from Bangladesh University of Engineering and Technology in 2001. \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/redefining-productivity-to-adapt-to-a-changing-landscape-of-work-shamsi-iqbal/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201207T100000
DTEND;TZID=America/New_York:20201207T110000
DTSTAMP:20260814T052449
CREATED:20201125T224350Z
LAST-MODIFIED:20201125T224355Z
UID:10000183-1607335200-1607338800@archive.sigchi.org
SUMMARY:Guidelines for Human-AI Interaction: Mihaela Vorvoreanu
DESCRIPTION:Title: Guidelines for Human-AI Interaction \nAbstract: In this talk\, Mihaela will present the work of synthesizing 20+ years on literature on human-AI and mixed initiative systems into 18 guidelines for human-AI interaction. She will introduce the guidelines to the audience and discuss their applications and implications for the design of user-facing AI systems. \nSpeaker Biography: Dr. Mihaela Vorvoreanu is head of UX research for Aether – Microsoft’s advisory committee on AI\, Ethics and Effects in Engineering and Research. Previous to joining Microsoft\, Mihaela was on the faculty at Purdue University\, where she founded and led undergraduate and graduate education in UX Design. \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/guidelines-for-human-ai-interaction-mihaela-vorvoreanu/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T030000
DTEND;TZID=America/New_York:20201130T040000
DTSTAMP:20260814T052449
CREATED:20201125T224050Z
LAST-MODIFIED:20201125T224050Z
UID:10000182-1606705200-1606708800@archive.sigchi.org
SUMMARY:Bias in Human-in-the-loop Artificial Intelligence - Gianluca Demartini
DESCRIPTION:Title: Bias in Human-in-the-loop Artificial Intelligence \nAbstract: Paid micro-task crowdsourcing has gained popularity also thanks to the rise of AI because of the convenient way to generate large-scale manually annotated corpora and because of the possibility to create human-in-the-loop systems. However\, when using crowdsourcing platforms for data gathering purposes\, human factors need to be taken into account as humans now become part of (and are not just users of) the system. \nIn this talk I will discuss our recent research in the area of micro-task crowdsourcing with a focus on understanding crowd worker behaviors and their implications on the quality of the collected data and the bias in it. I will first discuss open challenges in the crowdsourcing ecosystem including issues caused by adversarial approaches that may disrupt the crowdsourcing model as we know it. I will then discuss how human bias is reflected in the data which is being collected by means of crowdsourcing. Finally\, I will present our work making use of fine-grained behavioral logs. \nSpeaker Biography: Dr. Gianluca Demartini is an Associate Professor in Data Science at the University of Queensland\, School of Information Technology and Electrical Engineering. His main research interests are Information Retrieval\, Semantic Web\, and Human Computation. His research has been supported by the Australian Research Council (ARC)\, the UK Engineering and Physical Sciences Research Council (EPSRC)\, and by the EU H2020 framework program. He received Best Paper Awards at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018 and at the European Conference on Information Retrieval (ECIR) in 2016\, the Best Short Paper award at ECIR in 2020 and the Best Demo Award at the International Semantic Web Conference (ISWC) in 2011. He has published more than 100 peer-reviewed scientific publications including papers at major venues such as WWW\, ACM SIGIR\, VLDBJ\, ISWC\, and ACM CHI. \nHe has given several invited talks\, tutorials\, and keynotes at a number of academic conferences (e.g.\, ISWC\, ICWSM\, WebScience\, and the RuSSIR Summer School)\, companies (e.g.\, Facebook)\, and Dagstuhl seminars. He is a senior member of the ACM since 2020\, an ACM Distinguished Speaker since 2015\, and has been a TEDx speaker in 2019. \nHe serves as co-editor in chief for the Human Computation journal\, area editor for the Journal of Web Semantics\, and editorial board member for the Information Retrieval journal. He is General co-Chair for the ACM International Conference on Information and Knowledge Management (CIKM) 2021. He has been Senior Program Committee member for\, among others\, the ACM Conference on Research and Development in Information Retrieval (SIGIR)\, the ACM Web Search and Data Mining (WSDM) Conference\, the AAAI Conference on Human Computation and Crowdsourcing (HCOMP)\, and the International Conference on Web Engineering (ICWE). He is Program Committee member for several conferences including WWW\, SIGIR\, KDD\, IJCAI\, AAAI\, ISWC\, and ICWSM. He was Crowdsourcing and Human Computation Track co-Chair at WWW 2018 and co-chair for the Human Computation and Crowdsourcing Track at ESWC 2015. He co-organized several workshops and tutorials at international conferences as well as the Entity Ranking Track at the Initiative for the Evaluation of XML Retrieval in 2008 and 2009. \nBefore joining the University of Queensland\, he was Lecturer at the University of Sheffield in UK\, post-doctoral researcher at the eXascale Infolab at the University of Fribourg in Switzerland\, visiting researcher at UC Berkeley\, junior researcher at the L3S Research Center in Germany\, and intern at Yahoo! Research in Spain. In 2011\, he obtained a Ph.D. in Computer Science at the Leibniz University of Hanover focusing on Semantic Search. \nHomepage: http://www.gianlucademartini.net/ \nRSVP: https://www.academicfringe.org/registration
URL:https://archive.sigchi.org/event/bias-in-human-in-the-loop-artificial-intelligence-gianluca-demartini/
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201123T100000
DTEND;TZID=America/New_York:20201123T110000
DTSTAMP:20260814T052449
CREATED:20201120T183419Z
LAST-MODIFIED:20201120T185134Z
UID:10000029-1606125600-1606129200@archive.sigchi.org
SUMMARY:Matthew Lease - The Academic Fringe Festival
DESCRIPTION:Title: Adventures in Crowdsourcing: Toward Safer Content Moderation and Better Supporting Complex Annotation Tasks \nAbstract: I’ll begin the talk discussing content moderation. While most user content posted on social media is benign\, other content\, such as violent or adult imagery\, must be detected and blocked. Unfortunately\, such detection is difficult to automate\, due to high accuracy requirements\, costs of errors\, and nuanced rules for acceptable content. Consequently\, social media platforms today rely on a vast workforce of human moderators. However\, mounting evidence suggests that exposure to disturbing content can cause lasting psychological and emotional damage to some moderators. To mitigate such harm\, we investigate a set of blur-based moderation interfaces for reducing exposure to disturbing content whilst preserving moderator ability to quickly and accurately flag it. We report experiments with Mechanical Turk workers to measure moderator accuracy\, speed\, and emotional well-being across six alternative designs. Our key findings show interactive blurring designs can reduce emotional impact without sacrificing moderation accuracy and speed. See our online demo at: http://ir.ischool.utexas.edu/CM/demo/. \nThe second part of my talk will discuss aggregation modeling. Though many models have been proposed for binary or categorical labels\, prior methods do not generalize to complex annotations (e.g.\, open-ended text\, multivariate\, or structured responses) without devising new models for each specific task. To obviate the need for task-specific modeling\, we propose to model distances between labels\, rather than the labels themselves. Our models are largely agnostic to the distance function; we leave it to the requesters to specify an appropriate distance function for their given annotation task. We propose three models of annotation quality\, including a Bayesian hierarchical extension of multidimensional scaling which can be trained in an unsupervised or semi-supervised manner. Results show the generality and effectiveness of our models across diverse complex annotation tasks: sequence labeling\, translation\, syntactic parsing\, and ranking. \nHomepage: https://www.ischool.utexas.edu/~ml/. \nSpeaker Biography: Matthew Lease is an Associate Professor in the School of Information at the University of Texas at Austin\, where he is co-leading Good Systems (http://goodsystems.utexas.edu/)\, an eight-year Grand Challenge to design responsible AI technologies. In addition\, Lease is an Amazon Scholar\, working on Amazon Mechanical Turk\, SageMaker Ground Truth and Augmented Artificial Intelligence (A2I). He also worked previously at CrowdFlower. Lease received the Best Paper award at the 2016 AAAI Human Computation and Crowdsourcing conference\, as well as three early career awards for crowdsourcing (NSF\, DARPA\, IMLS). From 2011-2013\, Lease co-organized the National Institute of Standards and Technology (NIST) Text Retrieval Conference (TREC) crowdsourcing track. \n\nRSVP: https://docs.google.com/forms/d/e/1FAIpQLSegSUQnDrs6VG9Wl8BTw8xY9Yg0HN1uph8CMP2a89-cT-RKBQ/viewform
URL:https://archive.sigchi.org/event/matthew-lease-the-academic-fringe-festival/
END:VEVENT
END:VCALENDAR