The WAO researches and audits work algorithms to empower workers. Companies increasingly rely on opaque, black-box algorithms to determine pay, scheduling, and access to work, while the data needed to understand and challenge these systems remains out of reach for workers and organizers. To address this gap, the WAO develops technology-enabled research tools, data infrastructure, and academic–organizer partnerships that support existing labor and worker organizations. By building meaningful relationships with worker organizers, crowdsourcing data, conducting transparent and policy-relevant research, and thinking deeply about the data workers need both now and in the next 5-20 years, the WAO aims to increase worker power, inform policymaking, and improve conditions for workers today and in the future.
More Versus Better: Artificial Intelligence, Incentives, and the Emerging Crisis in Peer Review | Organization Science
As the AI Task Force for Organization Science, we provide an early account of artificial intelligence’s (AI) impact on both submissions and reviews at a major academic journal. Submission volume ha...
A Polycentric Governance Lens on Data Infrastructures
Computer Supported Cooperative Work (CSCW) - Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management...
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.
Work Trend Index: Microsoft’s latest research on the ways we work.
The Work Trend Index provides data-driven insights to help people and organizations thrive amid ongoing change and disruption.
Collective action strategies in the age of AI w/ Nick Vincent from Data Leverage - The Blockchain Socialist
I spoke to Nick Vincent, assistant professor of computing science at Simon Fraser University and author of the Data Leverage substack, about what it actually means that AI systems are built on the collective output of humanity’s digital labor and what we can do about it. Nick has spent years researching how data functions as a bargaining tool, […]
darkshapes
Umbrella organization rethinking machine-learning technology as tools that work for people, not just corporations
Beyond Articles: Rethinking Diamond Open Access for a Data-Driven Research Future | CODATA Blog
What would it take for Diamond OA to evolve into a holistic, equitable, and data-rich scholarly ecosystem — one that meaningfully includes early-career researchers?
AI Is Turning Workplaces Into Hopeless Gridlock
Office workers find that they are being flooded by AI generated workslop that's paradoxically making them less productive.
Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
Many powerful computing technologies rely on implicit and explicit data contributions from the public. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern.
Incomplete Contracting and AI Alignment
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
Professionalising Community Management Roles in Interdisciplinary Research Projects
In this article we discuss community management in interdisciplinary research teams, focusing on recognising and professionalising roles referred to here as the Research Community Managers (RCM). Drawing insights and examples from research and data science projects, we discuss how RCM roles address some of the researchâs most pressing challenges, from promoting best practices for open research and reproducibility to engaging diverse stakeholders in community-led research and ensuring fair recognition for their contributions. We offer a Community Maturation Indicator and share examples of projects from The Alan Turing Institute, the UK's national institute for data science and Artificial Intelligence (AI), where institutionally supported RCM roles were established. With the aim to integrate RCM expertise in teams involved in data science and AI research, we provide an RCM Skills and Competencies Framework. We also propose a roadmap for professionalising RCM roles by improving recognition and rewards, potential career paths and organisational support structures. To systematically sustain and progress these roles, we recommend institutional investment in establishing RCM teams that are empowered to prioritise collaboration, transparency and community-based approaches in interdisciplinary projects, such as in data science and AI. As a team, RCMs are well placed to connect disparate teams, initiatives and resources across the organisation, building more resilient research communities that can achieve greater innovation, improved project outcomes and a strongly connected ecosystem, with impacts extending beyond their narrow contexts.
Can “Conscious Data Contribution” Help Users to Exert “Data Leverage” Against Technology Companies?
Tech users currently have limited ability to act on concerns regarding the negative societal impacts of large tech companies. However, recent work suggests that users can exert leverage using their role in the generation of valuable data, for instance by withholding their data contributions to intelligent technologies. We propose and evaluate a new means to exert this type of leverage against tech companies: "conscious data contribution" (CDC).
Microsoft exec called AI scraping ‘the largest theft of labor in human history,' new unredacted filings reveal | TechCrunch
Newly unsealed court filings show Microsoft privately called OpenAI's data practices "theft" while both companies scraped paywalled Times content, built datasets from it, and warned internally it would gut publishers.
AI-Generated “Workslop” Is Destroying Productivity
Despite a surge in generative AI use across workplaces, most companies are seeing little measurable ROI. One possible reason is because AI tools are being used to produce “workslop”—content that appears polished but lacks real substance, offloading cognitive labor onto coworkers. Research from BetterUp Labs and Stanford found that 41% of workers have encountered such AI-generated output, costing nearly two hours of rework per instance and creating downstream productivity, trust, and collaboration issues. Leaders need to consider how they may be encouraging indiscriminate organizational mandates and offering too little guidance on quality standards. To counteract workslop, leaders should model purposeful AI use, establish clear norms, and encourage a “pilot mindset” that combines high agency with optimism—promoting AI as a collaborative tool, not a shortcut.
Broadening Access to Data Science Education in High School and Higher Education through Open Source Tools, Infrastructure, and Training
Equitable data science education requires a multifaceted approach, involving high school and higher education, community involvement, and accessible tools. A renewed investment in public digital infrastructure is needed to support these efforts. Nonprofits play a crucial role in supporting these efforts, and increased representation in leadership can enhance their impact. By addressing these disparities, we can ensure a more inclusive future in data science.