arXiv subjects

Catherine D'Ignazio

Publications and source records attributed to Catherine D'Ignazio.

5 recordsLinked to original sources

arXiv2026

Reimagining Data Work: Participatory Annotation Workshops as Feminist Practice

AI systems depend on the invisible and undervalued labor of data workers, who are often treated as interchangeable units rather than collaborators with meaningful expertise. Critical scholars and practitioners have proposed alternative principles for data work, but few empirical studies examine how to enact them in practice. This paper bridges this gap through a case study of multilingual, iterative, and participatory data annotation processes with journalists and activists focused on news narratives of gender-related violence. We offer two methodological contributions. First, we demonstrate how workshops rooted in feminist epistemology can foster dialogue, build community, and disrupt knowledge hierarchies in data annotation. Second, drawing insights from practice, we deepen the analysis of existing feminist and participatory principles. We show that prioritizing context and pluralism in practice may require ``bounding'' context and working towards what we describe as a ``tactical consensus.'' We also explore tensions around materially acknowledging labor while resisting transactional researcher-participant dynamics. Through this work, we contribute to growing efforts to reimagine data and AI development as relational and political spaces for understanding difference, enacting care, and building solidarity across shared struggles.

cs.CY↗

arXiv2026

Funding AI for Good: A Call for Meaningful Engagement

Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI's potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face real-world deployment and sustainability challenges. While existing HCI literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the upstream funding agendas that influence project approaches. In this work, we conducted a reflexive thematic analysis of 35 funding documents, representing about $410 million USD in total investments. We uncovered a spectrum of conceptual framings of AI4SG and the approaches that funding rhetoric promoted: from biasing towards technology capacities (more techno-centric) to emphasizing contextual understanding of the social problems at hand alongside technology capacities (more balanced). Drawing on our findings on how funding documents construct AI4SG, we offer recommendations for funders to embed more balanced approaches in future funding call designs. We further discuss implications for how the HCI community can positively shape AI4SG funding design processes.

cs.CY↗

arXiv2024

"Come to us first": Centering Community Organizations in Artificial Intelligence for Social Good Partnerships

Artificial Intelligence for Social Good (AI4SG) has emerged as a growing body of research and practice exploring the potential of AI technologies to tackle social issues. This area emphasizes interdisciplinary partnerships with community organizations, such as non-profits and government agencies. However, amidst excitement about new advances in AI and their potential impact, the needs, expectations, and aspirations of these community organizations--and whether they are being met--are not well understood. Understanding these factors is important to ensure that the considerable efforts by AI teams and community organizations can actually achieve the positive social impact they strive for. Drawing on the Data Feminism framework, we explored the perspectives of community organization members on their partnerships with AI teams through 16 semi-structured interviews. Our study highlights the pervasive influence of funding agendas and the optimism surrounding AI's potential. Despite the significant intellectual contributions and labor provided by community organization members, their goals were frequently sidelined in favor of other stakeholders, including AI teams. While many community organization members expected tangible project deployment, only two out of 14 projects we studied reached the deployment stage. However, community organization members sustained their belief in the potential of the projects, still seeing diminished goals as valuable. To enhance the efficacy of future collaborations, our participants shared their aspirations for success, calling for co-leadership starting from the early stages of projects. We propose data co-liberation as a grounding principle for approaching AI4SG moving forward, positing that community organizations' co-leadership is essential for fostering more effective, sustainable, and ethical development of AI.

cs.HC↗

arXiv2024

Data Feminism for AI

This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. In Data Feminism (2020), we offered seven principles for examining and challenging unequal power in data science. Here, we present a rationale for why feminism remains deeply relevant for AI research, rearticulate the original principles of data feminism with respect to AI, and introduce two potential new principles related to environmental impact and consent. Together, these principles help to 1) account for the unequal, undemocratic, extractive, and exclusionary forces at work in AI research, development, and deployment; 2) identify and mitigate predictable harms in advance of unsafe, discriminatory, or otherwise oppressive systems being released into the world; and 3) inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world in which all of us can thrive.

cs.CY↗

arXiv2021

Media Cloud: Massive Open Source Collection of Global News on the Open Web

We present the first full description of Media Cloud, an open source platform based on crawling hyperlink structure in operation for over 10 years, that for many uses will be the best way to collect data for studying the media ecosystem on the open web. We document the key choices behind what data Media Cloud collects and stores, how it processes and organizes these data, and its open API access as well as user-facing tools. We also highlight the strengths and limitations of the Media Cloud collection strategy compared to relevant alternatives. We give an overview two sample datasets generated using Media Cloud and discuss how researchers can use the platform to create their own datasets.

cs.SI↗