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

Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat, Isabelle Augenstein

Submission Type: Regular Long Paper

Submission Track: Theme Track: Large Language Models and the Future of NLP

Submission Track 2: Ethics in NLP

Keywords: dual use, ai ethics, checklist, harms, survey

TL;DR: Dual use is ill-defined in NLP; based on a survey of NLP researchers and practitioners we propose a checklist for mitigating dual use

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.

Submission Number: 3505