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    <titleInfo>
        <title>Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing</title>
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    <name type="personal">
        <namePart type="given">Lucie-Aimée</namePart>
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        <namePart type="given">Arnav</namePart>
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    <name type="personal">
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            <namePart type="given">Kalika</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.932</identifier>
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            <start>13977</start>
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%0 Conference Proceedings
%T Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing
%A Kaffee, Lucie-Aimée
%A Arora, Arnav
%A Talat, Zeerak
%A Augenstein, Isabelle
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kaffee-etal-2023-thorny
%X 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.
%R 10.18653/v1/2023.findings-emnlp.932
%U https://aclanthology.org/2023.findings-emnlp.932
%U https://doi.org/10.18653/v1/2023.findings-emnlp.932
%P 13977-13998

Markdown (Informal)

[Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing](https://aclanthology.org/2023.findings-emnlp.932) (Kaffee et al., Findings 2023)

• Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing (Kaffee et al., Findings 2023)

ACL

• Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat, and Isabelle Augenstein. 2023. Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13977–13998, Singapore. Association for Computational Linguistics.