Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Thorny Roses : Investigating the Dual Use Dilemma in Natural Language Processing. / Kaffee, Lucie-aimée; Arora, Arnav; Talat, Zeerak; Augenstein, Isabelle.

Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 13977-13998.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Kaffee, L, Arora, A, Talat, Z & Augenstein, I 2023, Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing. in Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), pp. 13977-13998, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, 06/12/2023. https://doi.org/10.18653/v1/2023.findings-emnlp.932

APA

Kaffee, L., Arora, A., Talat, Z., & Augenstein, I. (2023). Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 13977-13998). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-emnlp.932

Vancouver

Kaffee L, Arora A, Talat Z, Augenstein I. Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing. In Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL). 2023. p. 13977-13998 https://doi.org/10.18653/v1/2023.findings-emnlp.932

Author

Kaffee, Lucie-aimée ; Arora, Arnav ; Talat, Zeerak ; Augenstein, Isabelle. / Thorny Roses : Investigating the Dual Use Dilemma in Natural Language Processing. Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. pp. 13977-13998

Bibtex

@inproceedings{af2b59cc72d345438fa4f09f35240eba,
title = "Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing",
abstract = "Dual use, the intentional, harmful reuse of technology and scientific artefacts, is an ill-defined problem within the context of Natural Language Processing (NLP). As large language models (LLMs) have advanced in their capabilities and become more accessible, the risk of their intentional misuse becomes more prevalent. To prevent such intentional malicious use, it is necessary for NLP researchers and practitioners to understand and mitigate the risks of their research. Hence, we present an NLP-specific definition of dual use informed by researchers and practitioners in the field. Further, we propose a checklist focusing on dual-use in NLP, that can be integrated into existing conference ethics-frameworks. The definition and checklist are created based on a survey of NLP researchers and practitioners.",
author = "Lucie-aim{\'e}e Kaffee and Arnav Arora and Zeerak Talat and Isabelle Augenstein",
year = "2023",
doi = "10.18653/v1/2023.findings-emnlp.932",
language = "English",
pages = "13977--13998",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
publisher = "Association for Computational Linguistics (ACL)",
address = "United States",
note = "2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

}

RIS

TY - GEN

T1 - Thorny Roses

T2 - 2023 Findings of the Association for Computational Linguistics: EMNLP 2023

AU - Kaffee, Lucie-aimée

AU - Arora, Arnav

AU - Talat, Zeerak

AU - Augenstein, Isabelle

PY - 2023

Y1 - 2023

N2 - Dual use, the intentional, harmful reuse of technology and scientific artefacts, is an ill-defined problem within the context of Natural Language Processing (NLP). As large language models (LLMs) have advanced in their capabilities and become more accessible, the risk of their intentional misuse becomes more prevalent. To prevent such intentional malicious use, it is necessary for NLP researchers and practitioners to understand and mitigate the risks of their research. Hence, we present an NLP-specific definition of dual use informed by researchers and practitioners in the field. Further, we propose a checklist focusing on dual-use in NLP, that can be integrated into existing conference ethics-frameworks. The definition and checklist are created based on a survey of NLP researchers and practitioners.

AB - Dual use, the intentional, harmful reuse of technology and scientific artefacts, is an ill-defined problem within the context of Natural Language Processing (NLP). As large language models (LLMs) have advanced in their capabilities and become more accessible, the risk of their intentional misuse becomes more prevalent. To prevent such intentional malicious use, it is necessary for NLP researchers and practitioners to understand and mitigate the risks of their research. Hence, we present an NLP-specific definition of dual use informed by researchers and practitioners in the field. Further, we propose a checklist focusing on dual-use in NLP, that can be integrated into existing conference ethics-frameworks. The definition and checklist are created based on a survey of NLP researchers and practitioners.

U2 - 10.18653/v1/2023.findings-emnlp.932

DO - 10.18653/v1/2023.findings-emnlp.932

M3 - Article in proceedings

SP - 13977

EP - 13998

BT - Findings of the Association for Computational Linguistics: EMNLP 2023

PB - Association for Computational Linguistics (ACL)

Y2 - 6 December 2023 through 10 December 2023

ER -

ID: 381510983