I am interested in how people learn, participate, and exercise judgment in data-rich and AI-mediated environments. My research focuses four areas: Augmented Expertise (How can we cultivate specialized expertise among distributed, non-expert participants through adaptive scaffolding and hybrid human–AI systems?), Hybrid Intelligence Systems (How can humans and AI systems learn together in ways that improve both human capability and machine performance?), Algorithmic and Data Justice (How can data-driven systems in the public sector operate equitably and reflect community values?), and Civic Data and Participation (How can communities meaningfully participate in data-driven governance and decision-making?)
This research explores how people develop domain-specific expertise in complex environments without formal training. Drawing on work in citizen science systems such as Gravity Spy, I examine how interfaces, feedback, and structured participation enable non-experts to engage in sophisticated analytical work and contribute to scientific discovery.
- Jackson, C. (2025). Please Say "Shibboleth": Socialization Through Language Adoption in Virtual Citizen Science. In Proceedings of the International AAAI Conference on Web and Social Media.
- Jackson, C., Østerlund, C., Crowston, K., Harandi, M., Allen, S., Bahaadini, S., ... & Zevin, M. (2020). Teaching citizen scientists to categorize glitches using machine learning guided training. Computers in Human Behavior, 105, 106198.
This work moves beyond traditional pipelines where humans simply label data for AI systems. Instead, I study co-learning environments where humans and algorithms iteratively improve one another, forming adaptive systems that evolve over time.
- Zevin, M., Jackson, C. B., Doctor, Z., Wu, Y., Østerlund, C., Johnson, L. C., ... & Téglás, B. (2024). Gravity Spy: lessons learned and a path forward. The European Physical Journal Plus, 139(1), 100.
- Østerlund, C., Crowston, K., Jackson, C. B., Wu, Y., Smith, A. O., & Katsaggelos, A. K. (2024). Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy. Citizen Science: Theory and Practice, 9(1).
This research examines how AI systems embed assumptions about fairness, legitimacy, and decision-making. It develops participatory approaches in which communities act as evaluators of algorithmic systems, bringing contextual knowledge and normative reasoning into oversight processes.
- Jackson, C., Ahmad, T., Raj, S. D., & Wu, N. (2026). Beyond Bias Detection: Community auditors and normative reasoning in AI oversight. Proceedings of the ACM on Human-Computer Interaction.
This work focuses on inequities in access to data and participation in civic processes. I study how environmental and public-sector data systems can better represent community knowledge, particularly in contexts of environmental justice and local decision-making.
- Jeong, E., Jackson, C., Pandey, S., & Chen, K. (2026, April). Seeing Like a Community: Public Perceptions of Data Use in Government. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems.