AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models

Malviya, Shrikant; Owens, Rebecca; Copilah-Ali, Jehana; Elliot, Karen; Farrand, Ben; Neesham, Cristina; Shi, Lei; Vlachokyriakos, Vasilis; Katsigiannis, Stamos; van Moorsel, Aad

Abstract

AGENCY is a multidisciplinary research team of academics with expertise in computer science (natural language processing, cybersecurity, artificial intelligence, human-computer interaction), law, business, economics, social sciences and media studies. Members of AGENCY are academics at prestigious institutions such as Newcastle University, Durham University, University of Birmingham, King's College London, Royal Holloway University of London, and University of Surrey. UK Research and Innovation supports our research through the Strategic Priority Fund as part of the Protecting Citizens Online programme. Grant title: AGENCY: Assuring Citizen Agency in a World with Complex Online Harms. Grant reference: EP/W032481/2

This call for evidence of the future of large language models (LLM) and regulation coincides with the work, expertise, and concerns of the AGENCY project, which focuses on assuring citizen agency in a world with complex online harms. We refer to citizen agency as the ability for people and society to be empowered through technology and tools that provide them with a sense of control and security in that space. Thus, we propose that people and society should be at the forefront of regulation and that regulation should aim to premeditate, mitigate, and respond to complex online harms in a way that empowers people and balances that empowerment with societal concerns (such as public health, safety and security) while ensuring respect for principles such as freedom of expression. Our team possesses specialised expertise in LLM, law, emerging technologies, and their non-regulatory solutions. Accordingly, it is our responsibility to submit our response to this call for evidence, as we are well-positioned to make a useful contribution in this area.

Citation

Malviya, S., Owens, R., Copilah-Ali, J., Elliot, K., Farrand, B., Neesham, C., Shi, L., Vlachokyriakos, V., Katsigiannis, S., & van Moorsel, A. (2023). AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models. House of Lords Communications and Digital Select Committee

• Report Type: Policy Document
• Online Publication Date: Sep 4, 2023
• Publication Date: Sep 4, 2023
• Deposit Date: Oct 2, 2024
• Publicly Available Date: Oct 4, 2024
• Public URL: https://durham-repository.worktribe.com/output/2943096
• Publisher URL: https://committees.parliament.uk/writtenevidence/124223/html/