Data Governance in the Age of Large-Scale Data-Driven Language Technology

Yacine Jernite; Huu Nguyen; Stella Biderman; Anna Rogers; Maraim Masoud; Valentin Danchev; Samson Tan; Alexandra Sasha Luccioni; Nishant Subramani; Isaac Johnson; Gerard Dupont; Jesse Dodge; Kyle Lo; Zeerak Talat; Dragomir Radev; Aaron Gokaslan; Somaieh Nikpoor; Peter Henderson; Rishi Bommasani; Margaret Mitchell

doi:10.1145/3531146.3534637

Data Governance in the Age of Large-Scale Data-Driven Language Technology

Yacine Jernite
, Huu Nguyen
, Stella Biderman
, Anna Rogers
, Maraim Masoud
, Valentin Danchev
, Samson Tan
, Alexandra Sasha Luccioni
, Nishant Subramani
, Isaac Johnson
, Gerard Dupont
, Jesse Dodge
, Kyle Lo
, Zeerak Talat
, Dragomir Radev
, Aaron Gokaslan
, Somaieh Nikpoor
, Peter Henderson
, Rishi Bommasani
, Margaret Mitchell

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	2022 ACM Conference on Fairness, Accountability, and Transparency
Number of pages	17
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery
Publication date	1 Jun 2022
Pages	2206-2222
ISBN (Print)	978-1-4503-9352-2
DOIs	https://doi.org/10.1145/3531146.3534637
Publication status	Published - 1 Jun 2022
Event	Conference on Fairness, Accountability, and Transparency - Seoul, Korea, Democratic People's Republic of Duration: 21 Jun 2022 → 24 Jun 2022 https://dl.acm.org/doi/proceedings/10.1145/3531146

Conference

Conference	Conference on Fairness, Accountability, and Transparency
Country/Territory	Korea, Democratic People's Republic of
City	Seoul
Period	21/06/2022 → 24/06/2022
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3531146

Series	FAccT '22

Keywords

Machine Learning
Large Language Models
Language Data Governance
Distributed Governance
International Research Collaboration

Access to Document

10.1145/3531146.3534637

https://dl.acm.org/doi/10.1145/3531146.3534637

Cite this

Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., Dupont, G., Dodge, J., Lo, K., Talat, Z., Radev, D., Gokaslan, A., Nikpoor, S., Henderson, P., Bommasani, R., & Mitchell, M. (2022). Data Governance in the Age of Large-Scale Data-Driven Language Technology. In 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 2206-2222). Association for Computing Machinery. https://doi.org/10.1145/3531146.3534637

@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",

note = "Conference on Fairness, Accountability, and Transparency, FAccT ; Conference date: 21-06-2022 Through 24-06-2022",

url = "https://dl.acm.org/doi/proceedings/10.1145/3531146",

}

Jernite, Y, Nguyen, H, Biderman, S, Rogers, A, Masoud, M, Danchev, V, Tan, S, Luccioni, AS, Subramani, N, Johnson, I, Dupont, G, Dodge, J, Lo, K, Talat, Z, Radev, D, Gokaslan, A, Nikpoor, S, Henderson, P, Bommasani, R & Mitchell, M 2022, Data Governance in the Age of Large-Scale Data-Driven Language Technology. in 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, FAccT '22, pp. 2206-2222, Conference on Fairness, Accountability, and Transparency, Seoul, Korea, Democratic People's Republic of, 21/06/2022. https://doi.org/10.1145/3531146.3534637

Data Governance in the Age of Large-Scale Data-Driven Language Technology. / Jernite, Yacine; Nguyen, Huu; Biderman, Stella et al.
2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: Association for Computing Machinery, 2022. p. 2206-2222 (FAccT '22).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - Data Governance in the Age of Large-Scale Data-Driven Language Technology

AU - Jernite, Yacine

AU - Nguyen, Huu

AU - Biderman, Stella

AU - Rogers, Anna

AU - Masoud, Maraim

AU - Danchev, Valentin

AU - Tan, Samson

AU - Luccioni, Alexandra Sasha

AU - Subramani, Nishant

AU - Johnson, Isaac

AU - Dupont, Gerard

AU - Dodge, Jesse

AU - Lo, Kyle

AU - Talat, Zeerak

AU - Radev, Dragomir

AU - Gokaslan, Aaron

AU - Nikpoor, Somaieh

AU - Henderson, Peter

AU - Bommasani, Rishi

AU - Mitchell, Margaret

PY - 2022/6/1

Y1 - 2022/6/1

N2 - 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.

AB - 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.

KW - Machine Learning

KW - Large Language Models

KW - Language Data Governance

KW - Distributed Governance

KW - International Research Collaboration

KW - Machine Learning

KW - Large Language Models

KW - Language Data Governance

KW - Distributed Governance

KW - International Research Collaboration

U2 - 10.1145/3531146.3534637

DO - 10.1145/3531146.3534637

M3 - Article in proceedings

SN - 978-1-4503-9352-2

T3 - FAccT '22

SP - 2206

EP - 2222

BT - 2022 ACM Conference on Fairness, Accountability, and Transparency

PB - Association for Computing Machinery

CY - New York, NY, USA

T2 - Conference on Fairness, Accountability, and Transparency

Y2 - 21 June 2022 through 24 June 2022

ER -

Data Governance in the Age of Large-Scale Data-Driven Language Technology

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this