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Existing literature has predominantly concentrated on the legal, ethical, governance, political, and socioeconomic aspects of AI regulation, often relegating the technological dimension to the periphery, reflecting the design, use, and development of AI regulatory frameworks that are technology-neutral. The emergence and widespread use of generative AI models present new challenges for public regulators aiming at implementing effective regulatory interventions. Generative AI operates on distinctive technological properties that require a comprehensive understanding prior to the deployment of pertinent regulation. This paper focuses on the recent case of the suspension of ChatGPT in Italy to explore the impact the specific technological fabric of generative AI has on the effectiveness of technology-neutral regulation. By drawing on the findings of an exploratory case study, this paper contributes to the understanding of the tensions between the specific technological features of generative AI and the effectiveness of a technology-neutral regulatory framework. The paper offers relevant implications to practice arguing that until this tension is effectively addressed, public regulatory interventions are likely to underachieve their intended objectives. • Literature has largely overlooked the technological dimension of AI regulation, neglecting the specificity of generative AI. • Regulators adopting technology-neutral regulatory frameworks fail to recognize the technological specificity of generative AI. • Practitioners must fully comprehend the ever-evolving algorithmic logic of generative AI to implement meaningful regulation. • Regulating the generative AI use, regulators can better balance legal compliance with harnessing generative AI capabilities.
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Antonio Cordella
University of Warsaw
Francesco Gualdi
Regent's University London
Government Information Quarterly
London School of Economics and Political Science
Regent's University London
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Cordella et al. (Sat,) studied this question.
synapsesocial.com/papers/6a10969710ed65f1d0fd0ba8 — DOI: https://doi.org/10.1016/j.giq.2024.101982