Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm

TL;DR

This paper investigates how dissolving monopolistic AI firms can promote user data autonomy, using the framework of Conscious Data Contribution and machine unlearning techniques to enable user control over data usage.

Contribution

It introduces the application of Conscious Data Contribution and machine unlearning to facilitate user autonomy during the dissolution of large AI models and datasets.

Findings

Fine-tuning and catastrophic forgetting can serve as effective machine unlearning methods.

User data autonomy can be enhanced through model unlearning during firm dissolution.

Simulation results support the feasibility of user-controlled data contribution.

Abstract

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.