Regulation and NLP (RegNLP): Taming Large Language Models
Catalina Goanta Nikolaos Aletras Ilias Chalkidis Sofia Ranchordas Gerasimos Spanakis
The scientific innovation in Natural Language Processing (NLP) and more
broadly in artificial intelligence (AI) is at its fastest pace to date. As
large language models (LLMs) unleash a new era of automation, important debates
emerge regarding the benefits and risks of their development, deployment and
use. Currently, these debates have been dominated by often polarized narratives
mainly led by the AI Safety and AI Ethics movements. This polarization, often
amplified by social media, is swaying political agendas on AI regulation and
governance and posing issues of regulatory capture. Capture occurs when the
regulator advances the interests of the industry it is supposed to regulate, or
of special interest groups rather than pursuing the general public interest.
Meanwhile in NLP research, attention has been increasingly paid to the
discussion of regulating risks and harms. This often happens without systematic
methodologies or sufficient rooting in the disciplines that inspire an extended
scope of NLP research, jeopardizing the scientific integrity of these
endeavors. Regulation studies are a rich source of knowledge on how to
systematically deal with risk and uncertainty, as well as with scientific
evidence, to evaluate and compare regulatory options. This resource has largely
remained untapped so far. In this paper, we argue how NLP research on these
topics can benefit from proximity to regulatory studies and adjacent fields. We
do so by discussing basic tenets of regulation, and risk and uncertainty, and
by highlighting the shortcomings of current NLP discussions dealing with risk
assessment. Finally, we advocate for the development of a new multidisciplinary
research space on regulation and NLP (RegNLP), focused on connecting scientific
knowledge to regulatory processes based on systematic methodologies.