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.

翻译：当前人工智能在消费产品中的部署主要集中于使用所谓的基础模型，这些经过海量数字记录语料库预训练的大型神经网络。这种对数据集规模和预训练计算资源的强调加剧了行业进一步集中的风险，并可能催生垄断（或寡头垄断）行为。致力于改善市场竞争的法官与监管机构可采用多种救济措施。本文探讨将垄断实体拆分为若干小型企业的解体方案，特别聚焦于拆分大型模型与数据集所涉及的技术挑战与机遇。我们展示"自觉数据贡献"框架如何在解体过程中实现用户自主权。通过模拟研究，我们探讨微调与"灾难性遗忘"现象如何可能作为一种机器遗忘机制产生积极效果，使用户能够指定其数据的具体使用目的。