mlsanityData to 7 Oct 2026

Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

arXiv 2012.09995 ↗First posted 18 Dec 2020cs.CYcited by 25

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Abstract (arXiv)

Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this textitdata 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. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 5; 2025: 9; 2026: 3.

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  1. 2023Performative Prediction: Past and Futurecited by 52
  2. 2023Algorithmic Collective Action in Machine Learningcited by 30
  3. 2025Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectificationcited by 17
  4. 2021SoK: Anti-Facial Recognition Technologycited by 16
  5. 2022Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASRcited by 7
  6. 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
  7. 2025Algorithmic Collective Action with Two Collectivescited by 7
  8. 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
  9. 2025Statistical Collusion by Collectives on Learning Platformscited by 6
  10. 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Modelscited by 6
  11. 2021Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attackscited by 5
  12. 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
13 more
  1. 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibilitycited by 3
  2. 2024Towards Data Governance of Frontier AI Modelscited by 3
  3. 2024Data Defenses Against Large Language Modelscited by 2
  4. 2025Look-Ahead Reasoning on Learning Platformscited by 2
  5. 2021Addressing Privacy Threats from Machine Learningcited by 1
  6. 2025FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizerscited by 1
  7. 2022Collective Obfuscation and Crowdsourcingcited by 0
  8. 2024Promoting User Data Autonomy During the Dissolution of a Monopolistic Firmcited by 0
  9. 2025Data Enrichment Work and AI Labor in Latin America and the Caribbeancited by 0
  10. 2025Conscious Data Contribution via Community-Driven Chain-of-Thought Distillationcited by 0
  11. 2026How Professional Visual Artists are Negotiating Generative AI in the Workplacecited by 0
  12. 2026A Statistical Framework for Algorithmic Collective Action with Multiple Collectivescited by 0
  13. 2026Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harmscited by 0