Corey Jackson

As AI is increasingly used in public services, concerns have grown about bias and misalignment with community values. To investigate how ordinary citizens reason about fairness in public-sector AI, we conducted a scenario-based study with 110 participants who evaluated 13 algorithmically-informed government decision systems. Drawing on their responses, we analyze how auditors evaluate risks, identify impacted groups, and navigate fairness trade-offs. Our findings reveal that participants do not audit arbitrarily; they adjust their evaluations to context, reason from values, and exhibit principled subjectivity by applying fairness orientations consistently while remaining sensitive to scenario-specific details. However, we also observe tensions between bias recognition and prioritization, suggesting a gap between awareness and actionable design focus. Building on literature in participatory AI, algorithmic impact assessments, and user-driven auditing, we argue that good auditors are not merely detectors of error but normative agents who surface latent value tensions. We conclude by offering design implications for audit tooling that supports individual and group deliberation, trade-off reasoning, and equity-centered decision-making in the oversight of public algorithms.

Citizen science projects aim to democratize research by inviting broad public participation, yet sustaining engagement depends as much on community experience as on individual motivation. This paper examines how volunteers perceive the Zooniverse platform across dimensions of belonging, respect, diversity, and responsiveness, and how those perceptions shape patterns of participation. Drawing on survey responses (N = 6,030) and linked to contribution (N = 2,605) data across more than 100 projects, we quantify how perceptions mediate participation intensity in relation to socio-demographic factors. We demonstrate that volunteers with positive perceptions of community belonging, respect, and diversity contribute substantially more than peers with neutral or disagreeable views. These perception-contribution links are robust across demographics and extend beyond known participation gaps, providing actionable guidance for designing inclusive citizen science platforms.

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. The convergence of citizen science and computing research aligns with federal directives and national interests. Convergence supports mandates in AI governance, national competitiveness, transparency, and efficient public service delivery, including “Accelerating Federal Use of AI through Innovation, Governance, and Public Trust” (M-25-21) and “Driving Efficient Acquisition of Artificial Intelligence in Government” (M-25-22). We also referenced National Academies of Sciences, Engineering, and Math (NASEM) reports, such as the “Human-AI Teaming: State-of-the-Art and Research Needs (2022)” [hereafter NASEM 2022]. 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.

We explore patterns of interaction with different learning resources (e.g., forums) to predict learning outcomes in an online citizen science project called Gravity Spy. To explore how volunteers engage with and benefit from these resources, we categorize them based on Sørensen's three forms of presence in learning environments: authority-subject, agent-centered, and communal presence. Methodologically, we apply sequence analysis to traces of volunteer interactions with the project to identify engagement patterns with these resources that predict learning. Our interpretation of these patterns is augmented by insights gleaned from interviews with volunteers about their work and use of learning resources. We find that early in the project, volunteers have only a simple task to learn, and completing that task is most predictive of their learning. At more advanced levels, when tasks become more complex, discussions with other volunteers become increasingly important, and interaction patterns become more varied. Viewing learning as a series of routines allows us to articulate precisely how and in what context learning occurs. We conclude by discussing the implications of these findings for designing citizen science projects that promote learning.

The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena.

Web-accessible biodiversity databases accept and openly share species observations from the public, which benefits research, conservation, and education. However, public data sharing can also bring harm, for example by facilitating poaching. Databases may mitigate potential harms associated with data sharing by designating certain species as “sensitive” and restricting access to those species data. Herein, we explore how databases explain those restrictions through rationales. We analyzed rationale communication in 43 biodiversity databases that automatically restrict access to certain participatory science species data. We found a small set of commonly used rationales, wide variation in the number of rationales provided, and a surprising number of databases citing few rationales. We distinguish between general theme rationales that can apply to many species and specific theme rationales that apply to fewer species, and between low- and high-context rationales. Most databases provided general theme rationales at the database level, and a smaller group provided rationales (general or specific theme) unique to each species. Most databases explained restrictions in formal policies, but some did not. We discuss implications of rationale communication for data accessibility, risk management, and informed participation in participatory science, and link our findings to ongoing metadata standardization efforts. We suggest seven best practices for data restriction communication that account for differences in project values, obligations, and resources. Our primary recommendations are that databases provide rationales for data restrictions, ideally unique to each species, and make these rationales publicly accessible and easy to locate when doing so does not increase threats.