We’re in the middle of the most significant change to work practices that we’re likely to see in our lifetimes, and there’s a lot human computation and crowdsourcing can teach us about the future of work. In this talk I will discuss some of the ways emerging work practices following the move to remote and hybrid echo what’s been studied by the HCOMP community, and show how microproductivity can enable us to apply what we’ve learned to personal productivity contexts.
Jaime Teevan is a corporate vice president and Chief Scientist at Microsoft, where she is responsible for driving research-backed innovation in the company's core products. Jaime is an advocate for finding smarter ways for people to make the most of their time, and believes in the positive impact that breaks and recovery have on productivity. She leads Microsoft's future of work initiative which brings researchers from Microsoft, LinkedIn and GitHub together to study how the pandemic has changed the way people work. Previously she was Technical Advisor to CEO Satya Nadella and led the Productivity team at Microsoft Research.
Crowdsourcing and human computation have been particularly valuable tools in enabling novel social interaction at scale. Over the past 10 years, I have built, deployed, and studied dozens of crowd-powered interactive systems for a variety of domains: education, civic engagement, recipe analytics, conference planning, etc. These systems are organically structured: a crowd of users engages in a meaningful experience for themselves while collaboratively generating useful data and content for their own community. I will share hard-earned lessons from working on these systems and discuss how crowd-powered systems may truly scale by considering diverse user needs, values, and contexts.
Juho Kim is an Associate Professor in the School of Computing at KAIST and a director of KIXLAB. His research in human-computer interaction aims to improve the ways people learn, collaborate, discuss, and make decisions online by designing and building interactive systems that leverage and support interaction at scale. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 12 paper awards at top conferences in HCI. He earned his Ph.D. from MIT, M.S. from Stanford University, and B.S. from Seoul National University.
University of Oxford/Zooniverse
In the last decade or so, human-computer systems, in the form of distributed ‘citizen science’ projects, has made a real impact on scientific progress in fields as different as astrophysics and zoology. Drawing on lessons from the Zooniverse platform, which invites its two million registered volunteers to participate in hundreds of such projects, this talk will reflect on what is easy and what is hard about constructing such scientific social machines. We will consider optimising for impact, the desires and needs of participants, and - given the rise in the capability of computer vision, especially through deep learning - consider what, if any, future such projects have in the decades to come.
Chris Lintott is a professor of astrophysics at the University of Oxford, where he is also a research fellow at New College, working on topics including galaxy evolution, transient detection and machine learning. As Principal Investigator of the Zooniverse, he helps more than two million people to contribute to science. He is also the founder of Zooniverse spin-out company, 1715 Labs. A passionate advocate of the public understanding of science, he is the co-presenter of the BBC's long running Sky at Night program. His book, 'The Crowd and the Cosmos', is now available from Oxford University Press.
In her keynote speech Olga Megorskaya, CEO of Toloka, will talk about the evolving nature of crowdsourcing and how it is effectively applied in a wide range of different domains to solve various business challenges. Having started working with data labeling production more than ten years ago and having helped hundreds of teams set up thousands of projects at the helm of Toloka, Olga will share real-life use cases, highlight the importance of crowd science, and answer questions from the audience.
CEO of crowdsourcing platform Toloka.ai. Has been in charge of providing all Yandex products with data labelling & crowdsourcing solutions for the last 10 years. Co-author of research papers, tutorials & workshops on efficient crowdsourcing and quality control at SIGIR, KDD, CVPR, WSDM, and SIGMOD, and led the panel discussion at the Crowd Science workshops at NeurIps’20 and VLDB’21.
Amazon Web Services (AWS)
Artificial Intelligence (Software 2.0) has arrived. Unlike Software 1.0, Software 2.0 is data centric and uses machine learning (ML) to develop complex and intelligent systems that power many of our application today. However, the development of Software 2.0 is still very cumbersome, error prone, and slow. Today's ML model development lifecycle resembles the Waterfall development model of Software 1.0 from the 1990s. I will talk about how one can develop ML models faster and ship ML solutions earlier to their customers by using human-in-loop (HIL) approaches. I argue that while Software 2.0 expands from (code) to (code + data), Agile Software 2.0 needs to think holistically about (code + data + humans). In this holistic setup, humans not only label data that is used to train ML models, but ML also assists humans during labeling and cleans up labeled data to produce better quality models. I walk through Agile Software 2.0 designs for model training in the lab and model inference in production using exiting cloud services and crowd offerings.
Kumar Chellapilla is a General Manager at Amazon Web Services and leads the development of ML/AI Services that involve humans and machines working together. Prior to AWS, Kumar was a Director of Engineering at Uber ATG and Lyft Level 5 and led teams using machine learning to develop self-driving capabilities such as perception and mapping. He also worked on applying machine learning techniques to improve search, recommendations, and advertising products at LinkedIn, Twitter, Bing, and Microsoft Research.
Kumar has a Ph.D from University of California at San Diego wherein he worked on teaching computers to learn by themselves to play games like chess and checkers. At Microsoft Research, Kumar developed the very first GPU based algorithms (2004) for training Convolutional Neural Networks (CNNs). He then used them to demonstrate that CNNs surpassed human capabilities at single character recognition by breaking several commonly used CAPTCHAs.
University of Washington
Online social media platforms have brought numerous positive changes, including access to vast amounts of news and information. Yet, those very opportunities have created new challenges—our information ecosystem is now rife with problematic content, ranging from misinformation, conspiracy theories, to hateful and incendiary propaganda. As a social computing researcher, my work introduces computational methods and systems to understand and design defenses against such problematic online content. In this talk, I will focus on two aspects of problematic online information: 1) conspiracy theories and 2) extremist propaganda.
First, leveraging data spanning millions of conspiratorial posts on Reddit, 4chan, and 8chan, I will present scalable methods to unravel who participates in online conspiratorial discussions, what causes users to join conspiratorial communities and then potentially abandon them. Second, I will dive into a special type of problematic content: extremist hate groups. Merging theories from social movement research with big data analyses, I will discuss the ecosystem of extremists’ communication and the roles played by them. Finally, I will close by previewing important new opportunities to address some of these problems, including conducting social audits to defend against algorithmically generated misinformation and designing socio-technical interventions to promote meaningful credibility assessment of information.
Tanu Mitra is an Assistant Professor at the University of Washington, Information School, where she leads the Social Computing research group. She and her students study and build large-scale social computing systems to understand and counter problematic information online. Her research spans auditing online systems for misinformation and conspiratorial content, understanding digital misinformation in the context of the news ecosystem, unraveling narratives of online extremism and hate, and building technology to foster critical thinking online. Her work employs a range of interdisciplinary methods from the fields of human computer interaction, data mining, machine learning, and natural language processing.
Dr. Mitra’s work has been supported by grants from the NSF, DoD, Social Science One, and other Foundations. Her research has been recognized through multiple awards and honors, including an NSF-CRII, an early career ONR-YIP, Adamic-Glance Distinguished Young Researcher award and Virginia Tech College of Engineering Outstanding New Assistant Professor award, along with several best paper honorable mention awards. Dr. Mitra received her PhD in Computer Science from Georgia Tech’s School of Interactive Computing and her Masters in Computer Science from Texas A&M University.
We welcome everyone who is interested in crowdsourcing and human computation to: