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.