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.

翻译：从用户处获得有意义且知情的同意对于确保个人数据的自主权和控制权至关重要。作为收集同意的标准，通知与同意机制一直备受批评。尽管已有其他个体化解决方案被提出，但本文认为集体同意方法值得探索。首先，并非所有数据收集场景都具备收集个体同意的可行性。其次，鉴于某些数据的互联特性，数据处理造成的损害往往具有公共属性。最后，确保每个个体都实现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机制，作为一种替代性框架。我们建立了集体同意的理论基础，并运用思辨设计方法，构想利用审议式微公众的同意大会。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。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情境下的潜在应用。