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: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

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 (US)
Title of host publication	Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Publisher	Association for Computing Machinery
Pages	2206-2222
Number of pages	17
ISBN (Electronic)	9781450393522
DOIs	https://doi.org/10.1145/3531146.3534637
State	Published - Jun 21 2022
Externally published	Yes
Event	5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Korea, Republic of Duration: Jun 21 2022 → Jun 24 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Country/Territory	Korea, Republic of
City	Virtual, Online
Period	6/21/22 → 6/24/22

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Keywords

data rights
datasets
language data
technology governance

Access to Document

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 Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 (pp. 2206-2222). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3531146.3534637

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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 = "data rights, datasets, language data, technology governance",

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",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 ; Conference date: 21-06-2022 Through 24-06-2022",

year = "2022",

month = jun,

day = "21",

doi = "10.1145/3531146.3534637",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

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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 Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 2206-2222, 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022, Virtual, Online, Korea, Republic of, 6/21/22. 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.
Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022. Association for Computing Machinery, 2022. p. 2206-2222 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 - 2022/6/21

Y1 - 2022/6/21

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 - data rights

KW - datasets

KW - language data

KW - technology governance

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DO - 10.1145/3531146.3534637

M3 - Conference contribution

AN - SCOPUS:85132971169

T3 - ACM International Conference Proceeding Series

SP - 2206

EP - 2222

BT - Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

PB - Association for Computing Machinery

T2 - 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

Y2 - 21 June 2022 through 24 June 2022

ER -

Jernite Y, Nguyen H, Biderman S, Rogers A, Masoud M, Danchev V et al. Data Governance in the Age of Large-Scale Data-Driven Language Technology. In Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022. Association for Computing Machinery. 2022. p. 2206-2222. (ACM International Conference Proceeding Series). doi: 10.1145/3531146.3534637

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

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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