论文探讨如何在垄断企业拆分时保障用户数据自主权。
Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
- 提出意识数据贡献框架，让用户掌控数据使用
- 模拟显示灾难性遗忘可作为机器去识别的技术手段
- 适合关注数据主权与反垄断的政策研究者
当前人工智能在消费产品中的应用集中于大规模预训练的基础模型，这种对数据集和预训练算力的依赖加剧了行业集中化风险，助长垄断行为。为改善市场竞争，监管机构可考虑企业拆分这一补救措施。本文聚焦于大型模型与数据集拆分过程中的技术挑战与机遇，提出通过“意识数据贡献”框架实现用户在企业拆分期间的数据自主权。通过模拟研究发现，微调过程中出现的“灾难性遗忘”现象可被利用为一种机器去识别机制，使用户能够明确指定其数据可用于何种用途，从而增强数据控制能力。
原文摘要 · Abstract (English)
The deployment of AI in consumer products is currently focused on the use of so-called foundation models, large neural networks pre-trained on massive corpora of digital records. This emphasis on scaling up datasets and pre-training computation raises the risk of further consolidating the industry, and enabling monopolistic (or oligopolistic) behavior. Judges and regulators seeking to improve market competition may employ various remedies. This paper explores dissolution -- the breaking up of a monopolistic entity into smaller firms -- as one such remedy, focusing in particular on the technical challenges and opportunities involved in the breaking up of large models and datasets. We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution. Through a simulation study, we explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning that allows users to specify which data they want used for what purposes.
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