The 4th IEEE Workshop on
Human-in-the-Loop Methods and Future of Work in BigData
(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:

1. capturing human characteristics and capabilities,
2. stakeholder requirement specification,
3. social processes around the human-in-the-loop systems, 
4. platforms and ecosystems,
5. computation capabilities, and
6. benchmarks and metrics for human-in-the-loop systems and Future of Work

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.

• Yukino Baba (University of Tsukuba)
• Wolf-Tilo Balke (Technische Universitaet Braunschweig)
• Daniel Barowy (Williams College)
• Ria Mae Borromeo (University of the Philippines Open University)
• Pierre Bourhis (CNRS, CRIStAL)
• Francois Charoy (University of Lorraine, Inria, CNRS)
• Marina Danilevsky (IBM Research - Almaden)
• Gianluca Demartini (University of Queensland)
• Kyriaki Dimitradou (Amazon)
• Shady Elbassuoni (American University of Beirut)
• Ujwal Gadiraju (Delft University of Technology)
• David Gross Amblard (Rennes 1 University / IRISA Lab)
• Munenari Inoguchi (University of Toyama)
• Vana Kalogeraki (Athens University of Economics and Business)
• Itaru Kitahara (University of Tsukuba)
• Dongwon Lee (Pennsylvania State University)
• Masaki Matsubara (University of Tsukuba)
• Satoshi Oyama (Hokkaido University)
• Raghav Rao (University of Texas at San Antonio)
• Naoki Sakai (National Research Institute for Earth Science and Disaster Resilience)
• Nobuyuki Shimizu (Yahoo!Japan Research)
• Yu Suzuki (Gifu University)
• Keishi Tajima (Kyoto University)
• Saravanan Thirumuruganathan (QCRI)
• Demetris Zeinalipour (University of Cyprus)
• Jing Zhang Nanjing (U. of Science ＆ Technology)