Abstract
The paper uses aspirations mentioned in the initial research on machine learning decentralization as a lens for examining the current state-of-the-art and exposing opportunities for future innovations. We explore the potential and limitations of decentralized architectures in affording privacy and human agency for end users, competition, and collaboration for wider market and civic players. We then elaborate on the legal and technological developments necessary for decentralized machine learning systems to realize their liberating potential.
| Original language | English |
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| Title of host publication | CSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 16-23 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798400703331 |
| DOIs | |
| State | Published - 12 Mar 2024 |
| Event | 3rd Symposium on Computer Science and Law, CSLAW 2024 - Boston, United States Duration: 12 Mar 2024 → 13 Mar 2024 |
Publication series
| Name | CSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law |
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Conference
| Conference | 3rd Symposium on Computer Science and Law, CSLAW 2024 |
|---|---|
| Country/Territory | United States |
| City | Boston |
| Period | 12/03/24 → 13/03/24 |
Bibliographical note
Publisher Copyright:© 2024 Owner/Author.
Keywords
- competition
- decentralization
- federated learning
- governance
- machine learning
- privacy