Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
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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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- 2023Performative Prediction: Past and Futurecited by 52
- 2023Algorithmic Collective Action in Machine Learningcited by 30
- 2025Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectificationcited by 17
- 2021SoK: Anti-Facial Recognition Technologycited by 16
- 2022Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASRcited by 7
- 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
- 2025Algorithmic Collective Action with Two Collectivescited by 7
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
- 2025Statistical Collusion by Collectives on Learning Platformscited by 6
- 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Modelscited by 6
- 2021Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attackscited by 5
- 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
13 more
- 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibilitycited by 3
- 2024Towards Data Governance of Frontier AI Modelscited by 3
- 2024Data Defenses Against Large Language Modelscited by 2
- 2025Look-Ahead Reasoning on Learning Platformscited by 2
- 2021Addressing Privacy Threats from Machine Learningcited by 1
- 2025FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizerscited by 1
- 2022Collective Obfuscation and Crowdsourcingcited by 0
- 2024Promoting User Data Autonomy During the Dissolution of a Monopolistic Firmcited by 0
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbeancited by 0
- 2025Conscious Data Contribution via Community-Driven Chain-of-Thought Distillationcited by 0
- 2026How Professional Visual Artists are Negotiating Generative AI in the Workplacecited by 0
- 2026A Statistical Framework for Algorithmic Collective Action with Multiple Collectivescited by 0
- 2026Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harmscited by 0
- 2026Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms26 May 2026
- 2026A Statistical Framework for Algorithmic Collective Action with Multiple Collectives7 May 2026
- 2026How Professional Visual Artists are Negotiating Generative AI in the Workplace4 Mar 2026
- 2025Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation20 Dec 2025
- 2025Look-Ahead Reasoning on Learning Platforms18 Nov 2025
- 2025Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification13 Oct 2025
- 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy9 May 2025
- 2025Algorithmic Collective Action with Two Collectives30 Apr 2025
- 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models12 Mar 2025
- 2025FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers16 Feb 2025
- 2025Statistical Collusion by Collectives on Learning Platforms7 Feb 2025
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbean13 Jan 2025
13 more
- 2024Towards Data Governance of Frontier AI Models5 Dec 2024
- 2024Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm20 Nov 2024
- 2024Data Defenses Against Large Language Models17 Oct 2024
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Access19 May 2024
- 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists19 Mar 2024
- 2023Performative Prediction: Past and Future25 Oct 2023
- 2023Algorithmic Collective Action in Machine Learning8 Feb 2023
- 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibility3 Jan 2023
- 2022Collective Obfuscation and Crowdsourcing12 Aug 2022
- 2022Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR25 Feb 2022
- 2021SoK: Anti-Facial Recognition Technology8 Dec 2021
- 2021Addressing Privacy Threats from Machine Learning25 Oct 2021
- 2021Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks11 Jul 2021
- 2023Performative Prediction: Past and Futurecited by 52
- 2023Algorithmic Collective Action in Machine Learningcited by 30
- 2025Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectificationcited by 17
- 2021SoK: Anti-Facial Recognition Technologycited by 16
- 2022Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASRcited by 7
- 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
- 2025Algorithmic Collective Action with Two Collectivescited by 7
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
- 2025Statistical Collusion by Collectives on Learning Platformscited by 6
- 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Modelscited by 6
- 2021Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attackscited by 5
- 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
12 most cited of 24 All 24 (JSON)
- 2026Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms26 May 2026
- 2026A Statistical Framework for Algorithmic Collective Action with Multiple Collectives7 May 2026
- 2026How Professional Visual Artists are Negotiating Generative AI in the Workplace4 Mar 2026
- 2025Look-Ahead Reasoning on Learning Platforms18 Nov 2025
- 2025Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification13 Oct 2025
- 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy9 May 2025
- 2025Algorithmic Collective Action with Two Collectives30 Apr 2025
- 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models12 Mar 2025
- 2025FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers16 Feb 2025
- 2025Statistical Collusion by Collectives on Learning Platforms7 Feb 2025
- 2025Data Enrichment Work and AI Labor in Latin America and the Caribbean13 Jan 2025
- 2024Towards Data Governance of Frontier AI Models5 Dec 2024
12 newest of 24 All 24 (JSON)