Gualdi, Francesco and Cordella, Antonio ORCID: 0000-0002-4468-7807
(2024)
Theorizing the regulation of generative AI: lessons learned from Italy's ban on ChatGPT.
In: Bui, Tung X., (ed.)
Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024.
Proceedings of the Annual Hawaii International Conference on System Sciences.
IEEE Computer Society, pp. 2023-2032.
ISBN 9780998133171
![]() |
Text (Theorizing the regulation of generative AI: lessons learned from Italy’s ban on ChatGPT)
- Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (508kB) |
Abstract
Existing literature has predominantly concentrated on the legal and ethical aspects of government initiatives to regulate AI, often relegating the technological dimension to the periphery. However, the emergence and widespread use of generative AI models present new challenges for public regulators. Generative AI operates on distinctive technological properties which require a comprehensive understanding by regulators prior to the enactment of pertinent legislation. This paper focuses on the recent case of the Italian ban on ChatGPT to illustrate the public regulators' failure in acknowledging the unique characteristics intrinsic to generative AI, culminating in a flawed regulatory endeavour. By drawing on the findings of an exploratory case study, this paper contributes to the theoretical understanding of AI regulation, highlighting the discordance between the dynamism and fluidity of generative AI and the rigidity of regulatory frameworks. The paper contends that until this tension is effectively addressed, public regulatory interventions are likely to underachieve their intended objectives.
Item Type: | Book Section |
---|---|
Official URL: | https://scholarspace.manoa.hawaii.edu/items/6e0ca7... |
Additional Information: | © 2024 The Author(s) |
Divisions: | LSE |
Date Deposited: | 15 Aug 2024 23:39 |
Last Modified: | 28 Aug 2024 19:15 |
URI: | http://eprints.lse.ac.uk/id/eprint/124572 |
Actions (login required)
![]() |
View Item |