From Clicks to Consensus: Collective Consent Assemblies for Data Governance

TL;DR

This paper proposes a collective consent framework using consent assemblies to address the limitations of individual notice and consent, especially in complex data scenarios and communal harms.

Contribution

It introduces the concept of collective consent through deliberative mini-publics and explores its theoretical foundations and potential applications.

Findings

Collective consent can address infeasibility of individual consent in complex scenarios.

Consent assemblies enable deliberative, informed decision-making for data governance.

Future requirements for implementing collective consent are identified.

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.