Saiph Savage, Lea Shanley, Lucy Fortson, Corey Jackson, Kevin Crowston, Tanya Berger-Wolf, Haley Griffin. 2026. CI/HCOMP Conference’26, Sept 2026, Alexandria, VA, USA

Scientific discovery is increasingly constrained not by a lack of data, but by the challenge of turning massive, diverse, and constantly evolving datasets into knowledge that is meaningful and actionable. While advances across artificial intelligence (AI), computing and data sciences have created exciting new modes for scientific inquiry, these systems alone remain insufficient because they struggle with the contextual interpretation, local knowledge, or ethical judgment that complex scientific problems require. Our work proposes that the convergence of citizen science methods with AI, distributed computing and data sciences creates a new form of collective intelligence grounded in human-machine teaming, in which human communities and computational systems are intentionally designed to complement, correct, and extend one another. Rather than viewing citizen science merely as crowdsourced data collection or computational tools as autonomous infrastructure, we advocate for human-machine scientific systems in which citizen scientists, including distributed publics, participate as monitors, interpreters, decision-makers, and co-producers of knowledge. Through reimagining how scientific work is organized and interpreted, this contribution speaks to the CI 2026 theme of Connections by showing how scientific discovery can be redesigned as a connected process of human-machine collaboration across disciplines, communities, and computational systems.