From Clicks to Consensus: Collective Consent Assemblies for Data Governance

Paper Title

From Clicks to Consensus: Collective Consent Assemblies for Data Governance

Publication Info

• Topic area: Data governance and privacy consent mechanisms
• Keywords: collective consent, data privacy, deliberative mini-publics, consent assemblies, informed consent, GDPR, CCPA, speculative design, data governance, AI ethics

Background and Problem

• Problem / challenge: Current consent mechanisms, such as notice and consent, fail to provide meaningful and informed user consent due to usability issues, impracticality for all scenarios, and inability to address communal privacy implications.
• Significance: Addressing these shortcomings is critical for ensuring user autonomy, ethical data practices, and adapting to emerging technologies like generative AI.
• Motivation and related work: Existing alternatives, such as browser-level consent, reject-all extensions, and predictive consent, have limitations, including lack of granularity, trust issues, and inability to address collective privacy concerns. Prior works suggest collective governance as a promising approach but lack concrete frameworks for implementation.

Solution

• Proposed approach: The paper introduces collective consent operationalized through consent assemblies, leveraging deliberative mini-publics to make collective decisions about data governance.
• Novelty:
  1. Conceptualizing consent assemblies as a collective decision-making framework for data governance.
  2. Applying speculative design and future backcasting to envision practical implementations.
  3. Demonstrating applicability through two vignettes: replacing notice and consent, and addressing generative AI data reuse.
  4. Proposing systemic changes to legal, business, societal, and user perspectives for enabling collective consent.
• Procedure and key techniques:
  1. Adapting deliberative mini-publics for consent assemblies, including member selection, learning phases, deliberation, and outcomes.
  2. Speculative design to envision future applications and scenarios.
  3. Future backcasting to identify present-day changes needed for implementation.
  4. Developing vignettes to illustrate practical applications and challenges.

Results

• Concrete findings:
  • Consent assemblies can provide informed, participatory, and negotiable consent decisions.
  • Two vignettes demonstrated practical applications:
    1. Conditional consent for UX improvement purposes with restrictions on misuse.
    2. Rejection of data reuse for generative AI training due to ethical concerns and invalid prior consent.
• Advantage over baselines:
  • Addresses communal privacy implications and reduces user burden compared to notice and consent.
  • Provides a structured, participatory process for informed decision-making.
  • Simplifies regulatory compliance for businesses while protecting user rights.
• Experiments / evaluation:
  • Speculative design and vignettes were reviewed by experts in deliberative mini-publics to ensure realism and applicability.
• Limitations and future work:
  • Potential erosion of individual autonomy in favor of collective decisions.
  • Time-consuming implementation requiring systemic changes.
  • Risks of manipulation by vested interests.
  • Future work includes empirical studies, legal adaptations, and exploration of applications in smart homes, DNA data, and content moderation.

Summary

This paper proposes collective consent as an alternative to individual consent mechanisms, operationalized through consent assemblies based on deliberative mini-publics. The approach addresses limitations of notice and consent by enabling informed, participatory, and collective decision-making for data governance. Two vignettes illustrate its application to UX improvement and generative AI data reuse. While promising, collective consent requires systemic changes in regulation, societal attitudes, and business practices. Future work should focus on empirical validation, legal integration, and broader applications.