Reimagining Decentralized AI

Tomer Shadmy, Katrina Ligett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageAmerican English
Title of host publicationCSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law
PublisherAssociation for Computing Machinery, Inc
Pages16-23
Number of pages8
ISBN (Electronic)9798400703331
DOIs
StatePublished - 12 Mar 2024
Event3rd Symposium on Computer Science and Law, CSLAW 2024 - Boston, United States
Duration: 12 Mar 202413 Mar 2024

Publication series

NameCSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law

Conference

Conference3rd Symposium on Computer Science and Law, CSLAW 2024
Country/TerritoryUnited States
CityBoston
Period12/03/2413/03/24

Bibliographical note

Publisher Copyright:
© 2024 Owner/Author.

Keywords

  • competition
  • decentralization
  • federated learning
  • governance
  • machine learning
  • privacy

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