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.
In Pursuit of Ideal Data: Epistemic Enchantment and the Unintended Consequences of Datafying for Artificial Intelligence
The increasing datafication of work has become a pervasive theme within organizations, particularly with the rise of artificial intelligence (AI) technologies that rely on data and machine learning to generate insights and decisions. Prior research has shown that datafication practices aimed at administrative control and accountability can transform organizations in profound and often unintended ways. Our research moves beyond investigating datafication for administrative purposes to explore how it unfolds when actors intend to extract knowledge through AI predictive modeling. Drawing on a three-year ethnography of a human resources (HR) department introducing AI for candidate screening, we find that datafication expanded far beyond the technology’s initially envisioned scope. We explain this scope expansion by introducing the concept of epistemic enchantment : a collective process through which organizational members become captivated and mobilized by AI’s promise to generate predictive, objective, and continuously improving insights from data, while overlooking the costs and risks involved. Our study reveals how datafication practices can expand endlessly as organizational members, enchanted by AI’s epistemic promises, pursue the notion of ideal data as an ever-desired but never reachable goal.
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...
AI Data Centres - Platform | The Australian Greens
Labor has rolled out the red carpet for hyperscale AI data centres.
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...
Data Scientist: The Sexiest Job of the 21st Century
Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
Building pro-worker AI | Brookings
Daron Acemoglu, David Autor, and Simon Johnson ask: What is pro-worker AI, and how can we build it?
AI and Work (Some Predictions) - Cal Newport
One of the main topics of this newsletter is the quest to cultivate sustainable and meaningful work in a digital age. Given this objective, it’s ... Read more
Open-Ended Sessions: Workflow Archaeology
A conversation about how considered use of AI can help small and medium-sized businesses thrive.
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
Qualitative Research in an Era of Artificial Intelligence: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Computational developments—particularly artificial intelligence—are reshaping social scientific research and raising new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis, we revisit possibilities and dangers in an era of automation, large language model chatbots, and big data. We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science alongside in-depth observation, we describe methodological choices across study life cycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address long-standing methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet, a pragmatic approach—moving beyond technological optimism and dismissal—is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand, rather than displace, the irreducible insights of qualitative research.
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.
