The 4th IEEE Workshop on
|About||Keynotes||Dates and Submission||Program||Organization||HMData 2019||IEEE Bigdata 2020|
About IEEE HMData 2020
HMData workshop, which originally started as the "Human-Machine collaboration in BigData" workshop, will investigate the opportunities and challenges in human machine collaboration in work with bigdata, which are described by two terms: Human-in-the-Loop Methods and Future of Work. Human-in-the-Loop is a term focusing on the employer's viewpoint while Future of Work focuses more on worker's viewpoint, in both of which the division of labor among humans and machines is a key issue. This area is likely to be heavily AI driven, and we intend to invite papers covering the following aspects, (a) Capturing human capabilities through intelligent models and how to adapt them through changing perceptions, needs, and skills. (2) High level tools that provide the ability for all stakeholders in the new ecosystem, including regulators for policies and AI workers, to specify their requirements. (3) system design and engineering of job platforms for collection, storage, retrieval, and analysis of data deluge about workers, jobs, and their activities. (4) Benchmarking and the development of appropriate metrics to measure system performance as well as human aspects, such as satisfaction, capital advancement, and equity.
We welcome any interesting ideas and results on any relevant topics, but this year, we also encourage submitting papers on new projects inspired by the COVID-19 crisis, such as those on human-in-the-loop solutions in the pandemic, those on re-evaluating how we organize labor and how we share work with machines in the future. To make the workshop an attractive place for those people, we solicit practitioner papers as well as research papers, in order to facilitate discussion among researchers who know solutions and practitioners who know problems. We also would like to make the place valuable for young researchers. All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.
This workshop covers a wide range of topics of human-machine collaboration in work with bigdata. Keywords include: crowdsourcing, collaborative recommendation, crowdsensing, workflow model for humans and machines, incentives, human-assisted bigdata analysis, bigdata-human interaction, human-machine collaboration in real-world applications (such as natural disaster response, education, and citizen science), and ELSI in Human-in-the-loop systems and Future of Work. We expect submissions to address some of the following issues:
Crowd Sleuths: Solving Mysteries with Crowdsourcing, Experts, and AI
Kurt Luther (Virginia Tech)
Abstract: Professional investigators in fields such as journalism, law enforcement, and academia have long sought the public's help in solving mysteries, typically by providing tips. However, as social technologies capture more digital traces of daily life and enable new forms of collaboration, members of the public are increasingly leading their own investigations. These efforts are perhaps best known for high-profile failures characterized by sloppy research and vigilantism, such as the 2013 Boston Marathon Bombing manhunt on Reddit and 4chan. However, other crowdsourced investigations have led to the successful recovery of missing persons and apprehension of violent criminals, suggesting real potential. In this talk, I present three projects from my research group, the Crowd Intelligence Lab, where we build software tools that bring together crowds, experts, and AI to support ethical and effective investigations and solve mysteries. In the CrowdIA project, we adapted the sensemaking loop for intelligence analysts to enable novice crowds to discover a hidden terrorist plot within large quantities of textual evidence documents. In the GroundTruth project, we developed a novel diagramming technique to enable novice crowds to collaborate with expert investigators to geolocate and verify (or debunk) photos and videos shared on social media. In the Photo Sleuth project, we built and launched a free public website with over 10,000 registered users who employ AI-based face recognition to identify unknown soldiers in historical portraits from the American Civil War era. I will conclude the talk by discussing broader opportunities and risks in combining the complementary strengths of human and artificial intelligence for investigation, sensemaking, and other complex and creative tasks.
Bio: Dr. Kurt Luther is an associate professor of computer science and (by courtesy) history at Virginia Tech, based in the Washington, D.C. area. He directs the Crowd Intelligence Lab, creating new ways for experts to leverage the complementary strengths of crowdsourced human intelligence and artificial intelligence (AI) in domains like journalism, national security, and history. His current research focuses on supporting open source intelligence (OSINT) investigations, combating disinformation and misinformation, and identifying unknown people and places in historical and modern photos. Dr. Luther has been honored with the National Science Foundation CAREER Award, the Virginia Tech College of Engineering Outstanding New Assistant Professor Award, and the Purdue Polytechnic Institute Outstanding Technology Alumni Award. His papers have received the ACM CSCW Best Paper Award, the AAAI HCOMP Notable Paper Award, and the ACM IUI Best Paper Award. His software has won the Microsoft Cloud AI Research Challenge Grand Prize and two HCOMP Best Demo Awards. His research has been funded by DOD, Google, NEH, NHPRC, NIH, and NSF; and featured in The Atlantic, CNN, NPR, Smithsonian, and TIME. He is a member of AAAI and a senior member of ACM. Previously, Dr. Luther was a postdoctoral fellow in the Human-Computer Interaction Institute at Carnegie Mellon University. He received his Ph.D. in human-centered computing from Georgia Tech, where he was a James D. Foley Scholar. He received his B.S. in computer graphics technology, with honors and highest distinction, from Purdue University. He also completed internships at IBM Research, Microsoft Research, and YouTube/Google.
ProgramLabels: [FR] - Full Paper (Research-oriented), [FP] - Full Paper (Practice-oriented), [R] Project-in-Progress Paper (Research-oriented), [P] Project-in-Progress Paper (Practice-oriented)
(to appear in HTML soon)
SubmissionAll submissions must be submitted electorically through the submission page. Please prefix your submission category such as [Research Paper] to the Title of Paper field in the submission page. For example, if you would like to submit a project-in-progress paper "Crowd-centric Approach to Digital Archive Maintenance," you have to put "[project-in-progress paper] Crowd-centric Approach to Digital Archive Maintenance" into the Title of Paper field.
All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.
FormatPapers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines in the IEEE Bigdata 2020 CFP page
ChairsSenjuti Basu Roy (NJIT)
Alex Quinn (Pardue University)
Atsuyuki Morihsima (Univesity of Tsukuba)