Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop Report

This report is an outcome of a Computing Community Consortium (CCC) visioning workshop on Grand Challenges for the Convergence of Computational and Citizen Science Research conducted on April 8-9, 2025, in Washington, D.C. as well as through several precursor virtual input-gathering sessions. These events brought together experts across relevant disciplines to develop a research agenda that brings to fruition the above vision on how humans and machines may team up to solve some of the world's most pressing scientific problems. Citizen science delivers measurable economic and national value. Public participation in scientific research generates millions of dollars in volunteer labor value, extends government agency capacity, and directly supports federal priorities in areas such as disaster management, public health, water, energy, workforce development, and many more. At the same time, 21st-century scientific infrastructure requirements for citizen science (from hardware and cyberinfrastructure to data and computational frameworks) mirror those for computational science more generally. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world use case for a novel robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. In this report, we outline the key findings, future research directions, and recommendations that emerged from the April 2025 CCC Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop.

An epistemology for democratic citizen science

Abstract. More than ever, humanity relies on robust scientific knowledge of the world and our place within it. Unfortunately, our contemporary view of scie

The Citizen Lab - The Citizen Lab

The Citizen Lab is an interdisciplinary research unit at the Munk School of Global Affairs & Public Policy, University of Toronto. We apply our collective expertise in the fields of law, computer science, cybersecurity, political science, and social sciences to investigate complex issues of the 21st century. Explore focus areas Get the latest research in […]

Citizen science in environmental and ecological sciences

Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.

Mapping Citizen Science through the Lens of Human-Centered AI

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.

Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF

Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate

Governing by dismantling: tech oligarchy and the stifling of public data infrastructure

Published in Science as Culture (Ahead of Print, 2026)

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

AI and the Future of Digital Public Squares

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

A Roadmap to Democratic AI — The Collective Intelligence Project

Our roadmap outlines concrete steps that can be taken in 2024 to build a more democratic AI ecosystem that is adaptive, accountable, processes decentralized information, provides public goods, and safeguards human wellbeing. We describe what the ecosystem could build, research, advocate for, and fun

AI, peer review and the human activity of science

When researchers cede their scientific judgement to machines, we lose something important.

AI and the future of digital public squares

Two substantial technological advances have reshaped the public square in recent decades: first, the advent of the internet and second, the recent introduction of large language models (LLMs). LLMs offer opportunities to reimagine and redesign more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but LLMs without proper governance can also exacerbate societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. Given that people are shaped through their interactions in public squares, there is an important opportunity to build individual capacities and collective cohesion by transforming these digital public spaces. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

No shortcuts to research information citizenship - Digital Science

Being open isn't enough - true "research information citizenship" requires a robust, genuinely open research infrastructure.

Governing Together: Toward Infrastructure for Community-Run Social Media | Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Collaborative and social computing theory, concepts and paradigms