From Clicks to Consensus: Collective Consent Assemblies for Data Governance
🤖 AI Summary
This study addresses the limitations of traditional individualized “click-to-consent” mechanisms in data governance, which suffer from low feasibility, inadequate protection against collective data harms, and insufficient informed consent. To overcome these challenges, the paper proposes replacing individual consent with collective consent and innovatively develops a deliberative democracy–driven “Consent Assembly” model. By integrating speculative design, backcasting, and mini-public deliberation, the authors construct a systematic theoretical framework for collective consent. This framework not only expands the paradigmatic boundaries of data governance but also demonstrates practical promise in two key applications: first, as a viable alternative to the prevailing notice-and-consent regime, and second, as a pathway for collectively authorizing data use in generative artificial intelligence training, thereby exhibiting both feasibility and forward-looking potential.
Application Category
📝 Abstract
Obtaining meaningful and informed consent from users is essential for ensuring autonomy and control over one's data. Notice and consent, the standard for collecting consent, has been criticized. While other individualized solutions have been proposed, this paper argues that a collective approach to consent is worth exploring. First, individual consent is not always feasible to collect for all data collection scenarios. Second, harms resulting from data processing are often communal in nature, given the interconnected nature of some data. Finally, ensuring truly informed consent for every individual has proven impractical. We propose collective consent, operationalized through consent assemblies, as one alternative framework. We establish collective consent's theoretical foundations and use speculative design to envision consent assemblies leveraging deliberative mini-publics. We present two vignettes: i) replacing notice and consent, and ii) collecting consent for GenAI model training. Our paper employs future backcasting to identify the requirements for realizing collective consent and explores its potential applications in contexts where individual consent is infeasible.
Problem
Research questions and friction points this paper is trying to address.
informed consent
data governance
collective consent
communal harms
notice and consent
Innovation
Methods, ideas, or system contributions that make the work stand out.
collective consent
consent assemblies
deliberative mini-publics
speculative design
data governance
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