7 research outputs found
A collaborative citizen science platform for real-time volunteer computing and games
Volunteer computing (VC) or distributed computing projects are common in the
citizen cyberscience (CCS) community and present extensive opportunities for
scientists to make use of computing power donated by volunteers to undertake
large-scale scientific computing tasks. Volunteer computing is generally a
non-interactive process for those contributing computing resources to a project
whereas volunteer thinking (VT) or distributed thinking, which allows
volunteers to participate interactively in citizen cyberscience projects to
solve human computation tasks. In this paper we describe the integration of
three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a
job distribution middleware, and CitizenGrid, an online platform for hosting
and providing computation to CCS projects. This integration demonstrates the
combining of volunteer computing and volunteer thinking to help address the
scientific and educational goals of games like VAS. The paper introduces the
three tools and provides details of the integration process along with further
potential usage scenarios for the resulting platform.Comment: 12 pages, 13 figure
NLPositionality: Characterizing Design Biases of Datasets and Models
Design biases in NLP systems, such as performance differences for different
populations, often stem from their creator's positionality, i.e., views and
lived experiences shaped by identity and background. Despite the prevalence and
risks of design biases, they are hard to quantify because researcher, system,
and dataset positionality is often unobserved. We introduce NLPositionality, a
framework for characterizing design biases and quantifying the positionality of
NLP datasets and models. Our framework continuously collects annotations from a
diverse pool of volunteer participants on LabintheWild, and statistically
quantifies alignment with dataset labels and model predictions. We apply
NLPositionality to existing datasets and models for two tasks -- social
acceptability and hate speech detection. To date, we have collected 16,299
annotations in over a year for 600 instances from 1,096 annotators across 87
countries. We find that datasets and models align predominantly with Western,
White, college-educated, and younger populations. Additionally, certain groups,
such as non-binary people and non-native English speakers, are further
marginalized by datasets and models as they rank least in alignment across all
tasks. Finally, we draw from prior literature to discuss how researchers can
examine their own positionality and that of their datasets and models, opening
the door for more inclusive NLP systems.Comment: ACL 202
Instagram of Rivers: Facilitating Distributed Collaboration in Hyperlocal Citizen Science
Citizen science project leaders collecting field data in a hyperlocal community often face common socio-technical challenges, which can potentially be addressed by sharing innovations across different groups through peer-to-peer collaboration. However, most citizen science groups practice in isolation, and end up re-inventing the wheel when it comes to addressing these common challenges. This study seeks to investigate distributed collaboration between different water monitoring citizen science groups. We discovered a unique social network application called Water Reporter that mediated distributed collaboration by creating more visibility and transparency between groups using the app. We interviewed 8 citizen science project leaders who were users of this app, and 6 other citizen science project leaders to understand how distributed collaboration mediated by this app differed from collaborative practices of Non Water Reporter users. We found that distributed collaboration was an important goal for both user groups, however, the tasks that support these collaboration activities differed for the two user groups
Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges
Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)