Regulation of artificial intelligence

Regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI). The regulatory and policy landscape for AI is an emerging issue in jurisdictions worldwide, including for international organizations without direct enforcement power like the IEEE or the OECD.

Since 2016, numerous AI ethics guidelines have been published in order to maintain social control over the technology. Furthermore, organizations deploying AI have a central role to play in creating and implementing trustworthy AI, adhering to established principles, and taking accountability for mitigating risks. The European Union adopted in 2024 a common legal framework for AI with the AI Act.

AI governance is a term used in policy, industry, and academic contexts to describe how AI systems are directed and overseen. A 2025 systematic literature review defined it as encompassing the questions of who is accountable for AI systems, what elements are governed, when governance occurs within the development lifecycle, and how it is implemented through frameworks, tools, or models. Charlotte Stix noted that related terms, including "trustworthy AI", "responsible AI", and "ethical AI", have shifted in meaning over time and are often used interchangeably.

Background

Mitchell Waldrop used the term "machine ethics" in a 1987 article in AI Magazine. The AAAI held a symposium on machine ethics in 2005. Anna Jobin, Marcello Ienca, and Effy Vayena identified 84 sets of AI ethics guidelines published worldwide by 2019, 88% of which had been released after 2016. The guidelines showed agreement on five principles: transparency, justice, non-maleficence, responsibility, and privacy. The Partnership on AI was established in 2016 by Apple, Amazon, Google, Facebook, IBM, and Microsoft. The IEEE launched its Global Initiative on Ethics of Autonomous and Intelligent Systems in 2016. The initiative published Ethically Aligned Design in 2019.

According to Stanford University's 2025 AI Index, legislative mentions of AI rose 21.3% across 75 countries since 2023, marking a ninefold increase since 2016. The U.S. federal agencies introduced 59 AI-related regulations in 2024—more than double the number in 2023. In 2024, nearly 700 AI-related bills were introduced across 45 states, up from 191 in 2023.

There is currently no broad consensus on the degree or mechanics of AI regulation. Several prominent figures in the field, including Elon Musk, Sam Altman, Dario Amodei, and Demis Hassabis have publicly called for immediate regulation of AI. In 2023, following GPT-4's creation, Elon Musk and others signed an open letter urging a moratorium on the training of more powerful AI systems. Others, such as Mark Zuckerberg and Marc Andreessen, have warned about the risk of preemptive regulation stifling innovation.

In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".

In 2023, the United Kingdom started a series of international summits on AI with the AI Safety Summit. It was followed by the AI Seoul Summit in 2024, the AI Action Summit in Paris in 2025, and the AI Impact Summit in New Delhi in 2026.

Perspectives

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI. Public administration and policy considerations generally focus on the technical and economic implications and on trustworthy and human-centered AI systems, regulation of artificial superintelligence, the risks and biases of machine-learning algorithms, the explainability of model outputs, and the tension between open source AI and unchecked AI use.

There have been both hard law and soft law proposals to regulate AI. Some legal scholars have noted that hard law approaches to AI regulation have substantial challenges. Among the challenges, AI technology is rapidly evolving leading to a "pacing problem" where traditional laws and regulations often cannot keep up with emerging applications and their associated risks and benefits. Similarly, the diversity of AI applications challenges existing regulatory agencies, which often have limited jurisdictional scope. As an alternative, some legal scholars argue that soft law approaches to AI regulation are promising, as they offer greater flexibility to adapt to emerging technologies and the evolving nature of AI applications. However, soft law approaches often lack substantial enforcement potential.

Cason Schmit, Megan Doerr, and Jennifer Wagner proposed the creation of a quasi-governmental regulator by leveraging intellectual property rights (i.e., copyleft licensing) in certain AI objects (i.e., AI models and training datasets) and delegating enforcement rights to a designated enforcement entity. They argue that AI can be licensed under terms that require adherence to specified ethical practices and codes of conduct. (e.g., soft law principles).

Prominent youth organizations focused on AI, namely Encode AI, have also issued comprehensive agendas calling for more stringent AI regulations and public-private partnerships.

AI regulation could derive from basic principles. A 2020 Berkman Klein Center for Internet & Society meta-review of existing sets of principles, such as the Asilomar Principles and the Beijing Principles, identified eight such basic principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and respect for human values. AI law and regulations have been divided into three main topics, namely governance of autonomous intelligence systems, responsibility and accountability for the systems, and privacy and safety issues. A public administration approach sees a relationship between AI law and regulation, the ethics of AI, and 'AI society', defined as workforce substitution and transformation, social acceptance and trust in AI, and the transformation of human to machine interaction. The development of public sector strategies for management and regulation of AI is deemed necessary at the local, national, and international levels and in a variety of fields, from public service management and accountability to law enforcement, healthcare (especially the concept of a Human Guarantee), the financial sector, robotics, autonomous vehicles, the military and national security, and international law.

Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a jo…