HCOMP is the home of the human computation and crowdsourcing community. It’s the premier venue for presenting latest findings from research and practice into frameworks, methods and systems that bring together people and machine intelligence to achieve better results.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

Submissions may hence cover theory, studies, tools and applications that present novel, interesting, impactful interactions between people and computational systems. These cover a broad range of scenarios, from classical human computation, wisdom of the crowds, and all forms of crowdsourcing to people-centric AI methods, systems and applications.

Topics of interest include:

    Crowdsourcing applications and techniques, including but not limited to: citizen science, collective action, collective intelligence, wisdom of the crowds, crowdsourcing contests, crowd creativity, crowdfunding, paid microtasks, crowd ideation, crowd sensing, prediction markets.
    Techniques that enable and enhance human-in-the-loop systems, making them more efficient, accurate, and human-friendly, including task design, quality assurance, answer inference, biases and subjectivity, incentives, gamification, task allocation, complex workflows, real-time crowdsourcing etc.
    Approaches to make crowd science FAIR (Findable, Accessible, Interoperable, Reproducible) and studies assessing and commenting on the FAIRness of human computation and crowdsourcing practice.
    Studies into the reliability and other quality aspects of human-annotated and -curated datasets.
    Studies into replicability of crowdsourcing and human computation experiments.
    Methods that use human computation and crowdsourcing to build people-centric AI systems and applications, including topics such as explainability and interpretability.
    Studies about how people perform tasks individually, in groups, or as a crowd, including those drawing on techniques from human-computer interaction, social computing, computer-supported cooperative work, design, cognitive sciences, behavioral sciences, economics, etc.
    Studies into fairness, accountability, transparency, ethics, and policy implications for crowdsourcing and human computation.
    Studies that inform our understanding about the future of work, distributed work, the freelancer economy, open innovation and citizen-led innovation.