# Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm

arXiv: https://arxiv.org/abs/2411.13546 · HTML: https://mlsanity.com/paper/2411.13546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Merging Models with Fisher-Weighted Averaging](https://mlsanity.com/paper/2111.09832/) (2021)
- [Git Re-Basin: Merging Models modulo Permutation Symmetries](https://mlsanity.com/paper/2209.04836/) (2022)
- [Towards Unbounded Machine Unlearning](https://mlsanity.com/paper/2302.09880/) (2023)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020)
- [Against Scale: Provocations and Resistances to Scale Thinking](https://mlsanity.com/paper/2010.08850/) (2020)

## Abstract (arXiv)

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.