❦ Data Leverage & Collective Action
This collection gathers foundational and recent work on **data leverage**—the strategic use of data withholding, contribution, or manipulation as a form of collective action. ## Core Concepts - **Data Strikes**: Coordinated refusal to contribute data to platforms - **Data Poisoning**: Intentionally corrupting training data to degrade model performance - **Conscious Data Contribution**: Strategically directing data to preferred systems - **Data Valuation**: Methods for quantifying individual data contributions (Shapley values, etc.) ## Why This Matters As AI systems become more dependent on user-generated content and behavioral data, data creators gain potential leverage over technology companies. This research explores when and how such leverage can be effectively exercised.
20 papers in this collection
- Algorithmic Collective Action with Two Collectives
- The Economics of AI Training Data: A Research Agenda
- Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
- Push and Pull: A Framework for Measuring Attentional Agency on Digital Platforms
- Poisoning Web-Scale Training Datasets is Practical
- Large language models reduce public knowledge sharing on online Q&A platforms
- Algorithmic Collective Action in Machine Learning
- The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers
- Behavioral Use Licensing for Responsible AI
- Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
- Addressing Documentation Debt in Machine Learning Research: A Retrospective Datasheet for BookCorpus
- Machine Unlearning
- Extracting Training Data from Large Language Models
- Can "Conscious Data Contribution" Help Users to Exert "Data Leverage" Against Technology Companies?
- Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
- Data Shapley: Equitable Valuation of Data for Machine Learning
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
- How Do People Change Their Technology Use in Protest?: Understanding Protest Users
- "Data Strikes": Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies
- Examining Wikipedia With a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities