Obtaining meaningful and informed consent from users is essential for ensuring they maintain autonomy and control over their data. Notice and consent, the standard for collecting consent online, has been criticized. While other individualized solutions have been proposed, this paper argues that a collective approach to consent is worth exploring for several reasons. First, the data of different users is often interlinked, and individual data governance decisions may impact others. Second, harms resulting from data processing are often communal in nature. Finally, having every individual sufficiently informed about data collection practices to ensure truly informed consent has proven impractical. We propose collective consent, operationalized through consent assemblies, as one alternative framework. We establish the theoretical foundations of collective consent and employ speculative design to envision how consent assemblies could function by leveraging deliberative mini-publics. We present two vignettes: i) replacing notice and consent, and ii) collecting consent for GenAI model training, to demonstrate its wide application. 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.

翻译：获取用户有意义且知情的同意对于确保他们保持对数据的自主权和控制权至关重要。作为在线收集同意的标准，"通知与同意"机制一直受到批评。虽然已有其他个体化解决方案被提出，但本文认为，探索集体同意方法具有多方面价值。首先，不同用户的数据往往相互关联，个体数据治理决策可能影响他人。其次，数据处理造成的损害通常具有公共性质。最后，要求每个个体都充分了解数据收集实践以确保真正知情同意已被证明不切实际。我们提出通过同意大会实现的集体同意作为替代框架之一。我们建立了集体同意的理论基础，并运用思辨设计方法，借鉴协商式微型公众机制，构想同意大会的运作方式。我们呈现了两个应用场景：i) 替代通知与同意机制，ii) 为生成式人工智能模型训练收集同意，以展示其广泛适用性。本文采用未来回溯法识别实现集体同意的必要条件，并探讨其在个体同意不可行场景中的潜在应用。