Regulating ChatGPT and other Large Generative AI Models

By Philipp Hacker, Andreas Engel, M. Mauer. Conference on Fairness, Accountability and Transparency, 2023

486 citations recorded by the source corpus.

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA's content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers.

Subfields: Economics and Econometrics, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/bd166912fa16cec04e3ee044a0d0fb6e3ac6fe33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Luciano Floridi

In Health Informatics

1. Siyuan Ma
2. Gero Strobel
3. Luciano Floridi
4. Yiheng Liu
5. Antong Li
6. Zheng Liu
7. Haoyang He
8. Mengshen He

In Safety Research

1. Jonas Schuett
2. Siyuan Ma
3. Gero Strobel
4. Luciano Floridi
5. Yiheng Liu
6. Jakob Mokander
7. Antong Li
8. Zheng Liu

Citations in the corpus

• The potential of generative AI for personalized persuasion at scale (2024, background): “For example, academic proposals to regulate generative AI have collectively argued that regulation should occur at the stage of deployment (e.g., at the advertiser and platform level), rather than at the level of message reception (e.g., at the time point consumers are seeing the specific ad) 79 .”
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective (2023, background): “[18, 41] expressed concerns that ChatGPT and other large models should be regulated since they are double-edged swords.”
• Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023, background): “[87] discussed the nature and rules of large generative AI models, including ChatGPT, which are rapidly changing the way we communicate, explain, and create.”
• Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023, background): “Regarding relevant policies and regulations, Hacker et al. [25] discussed the nature and rules of large generative AI models, including ChatGPT, which are rapidly changing the way we communicate, explain, and create.”
• Large language models in healthcare and medical domain: A review (2023, background): “However, they also promote reliability, legal accountability, and ethical standards in AI development, fostering trust among users and stakeholders and encouraging the responsible and wider adoption of AI technologies in healthcare [135].”
• Generative artificial intelligence (2023, background): “…the European Union AI Act or US National AI Initiative) serve as promising initial steps toward opening pathways for future research to effectively address these issues, ensuring that AI-generated content is morally, ethically, and legally appropriate and cannot be mis-used (Hacker et al., 2023).”
• Auditing large language models: a three-layered approach (2023, background): “Therefore, the question of how LLMs should be governed has attracted much attention, with proposals ranging from structured access protocols designed to prevent malicious use [76] to hard regulation prohibiting the deployment of LLMs for specific purposes [86].”
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks (2023, unknown): “Indeed, the propensity of LLMs to reproduce this objectionable content has invigorated the field of AI alignment [2–4], wherein various mechanisms are used to “align” the output text generated by LLMs with ethical and legal standards [5–7].”
• Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024, unknown): “These generated datasets can reflect real-world complexities while preserving the privacy and anonymity necessary in many use cases (Hacker, Engel, and Mauer 2023).”
• The imperative for regulatory oversight of large language models (or generative AI) in healthcare (2023, unknown): “In details, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency, (ii) risk management, (iii) non-discrimination provisions, and (iv) content moderation rules 15 .”