@inproceedings{4468c5be95bb46788aa79fd35405ee79,
title = "Data Governance in the Age of Large-Scale Data-Driven Language Technology",
abstract = "The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. Our proposal is informed by prior work on distributed governance that accounts for human values and grounded by an international research collaboration that brings together researchers and practitioners from 60 countries. The framework we present is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work.",
keywords = "Machine Learning, Large Language Models, Language Data Governance, Distributed Governance, International Research Collaboration, Machine Learning, Large Language Models, Language Data Governance, Distributed Governance, International Research Collaboration",
author = "Yacine Jernite and Huu Nguyen and Stella Biderman and Anna Rogers and Maraim Masoud and Valentin Danchev and Samson Tan and Luccioni, {Alexandra Sasha} and Nishant Subramani and Isaac Johnson and Gerard Dupont and Jesse Dodge and Kyle Lo and Zeerak Talat and Dragomir Radev and Aaron Gokaslan and Somaieh Nikpoor and Peter Henderson and Rishi Bommasani and Margaret Mitchell",
year = "2022",
month = jun,
day = "1",
doi = "10.1145/3531146.3534637",
language = "English",
isbn = "978-1-4503-9352-2",
series = "FAccT '22",
pages = "2206--2222",
booktitle = "2022 ACM Conference on Fairness, Accountability, and Transparency",
publisher = "Association for Computing Machinery",
address = "United States",
}