Summary of Promoting User Data Autonomy During the Dissolution Of a Monopolistic Firm, by Rushabh Solanki et al.
Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
by Rushabh Solanki, Elliot Creager
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential risks of AI deployment in consumer products being controlled by large, pre-trained neural networks. It suggests that this could lead to monopolistic behavior and proposes dissolution as a possible remedy. The authors examine the technical challenges and opportunities involved in breaking up large models and datasets, including the concept of Conscious Data Contribution and its role in enabling user autonomy during dissolution. They also investigate the effects of fine-tuning and catastrophic forgetting on machine unlearning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how AI is used in everyday products. Right now, companies are using super-sized computer programs called foundation models to make these products. But this could lead to one company having too much control over what we use. To fix this, the authors suggest breaking up these big computer programs into smaller ones. They also explore ways for users to have more control over how their data is used. This includes a new way of thinking called Conscious Data Contribution that lets people choose which data they want used for certain things. |
Keywords
» Artificial intelligence » Fine tuning
