@inproceedings{7a9b8dbe9f484f72892fdf0c653c33d0,
title = "Reimagining Decentralized AI",
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.",
keywords = "competition, decentralization, federated learning, governance, machine learning, privacy",
author = "Tomer Shadmy and Katrina Ligett",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 3rd Symposium on Computer Science and Law, CSLAW 2024 ; Conference date: 12-03-2024 Through 13-03-2024",
year = "2024",
month = mar,
day = "12",
doi = "https://doi.org/10.1145/3614407.3643701",
language = "الإنجليزيّة",
series = "CSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law",
pages = "16--23",
booktitle = "CSLAW 2024 - Proceedings of the 3rd Symposium on Computer Science and Law",
}