Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP). However, as NLP technologies continue to advance and become increasingly widespread in society, their inner workings have become increasingly opaque. Therefore, understanding dual use concerns and potential ways of limiting them is critical to minimising the potential harms of research and development. In this paper, we conduct a survey of NLP researchers and practitioners to understand the depth and their perspective of the problem as well as to assess existing available support. Based on the results of our survey, we offer a definition of dual use that is tailored to the needs of the NLP community. The survey revealed that a majority of researchers are concerned about the potential dual use of their research but only take limited action toward it. In light of the survey results, we discuss the current state and potential means for mitigating dual use in NLP and propose a checklist that can be integrated into existing conference ethics-frameworks, e.g., the ACL ethics checklist.

The thought had never previously struck us. We were vaguely aware of security concerns around work with pathogens or toxic chemicals, but that did not relate to us; we primarily operate in a virtual setting. Our work is rooted in building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery. We have spent decades using computers and AI to improve human health--not to degrade it. We were naive in thinking about the potential misuse of our trade, as our aim had always been to avoid molecular features that could interfere with the many different classes of proteins essential to human life.

The rapid progress and development in artificial intelligence (AI) is prompting desperate speculation about its dual-use applications and security risks. From autonomous weapons systems (AWS) to facial recognition technology to decision-making algorithms, each emerging application of artificial intelligence brings with it both good and bad. It is this dual nature of artificial intelligence technology that is bringing enormous security risks to not only individuals and entities across nations: its government, industries, organizations, and academia (NGIOA) but also the future of humanity. The reality is that any new AI innovation might be used for both beneficial and harmful purposes: any single algorithm that may provide important economic applications might also lead to the production of unprecedented weapons of mass destruction on a scale that is difficult to fathom. As a result, the concerns about artificial intelligence-based automation are growing.

Contemplating the future..Depositphotos enhanced by CogWorld The rapid progress and development in artificial intelligence (AI) is prompting desperate speculation about its dual use applications and security risks. From autonomous weapons systems (AWS) to facial recognition technology to decision-making algorithms, each emerging application of artificial intelligence brings with it both good and bad. It is this dual nature of artificial intelligence technology that is bringing enormous security risks to not only individuals and entities across nations: its government, industries, organizations and academia (NGIOA) but also the future of humanity. The reality is that any new AI innovation might be used for both beneficial and harmful purposes: any single algorithm that may provide important economic applications might also lead to the production of unprecedented weapons of mass destruction on a scale that is difficult to fathom. As a result, the concerns about artificial intelligence-based automation are growing.