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 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 joint statement in November 2021 entitled "Being Human in an Age of AI", calling for a government commission to regulate AI.
A 2025 study found competition law and competition regulators face challenges in preventing a winner-take-all market for AI.
In 2025, the UK and US governments declined to sign an international agreement on AI at the AI Action Summit in Paris. The agreement was described as proposing an open, inclusive and ethical approach to AI development, including environmental protection measures. US Vice President JD Vance argued that the agreement would be detrimental to the growth of the AI industry. The UK government added that the agreement "didn't provide enough practical clarity on global governance, nor sufficiently address harder questions around national security".
As a response to the AI control problem
Regulation of AI can be seen as positive social means to manage the AI control problem (the need to ensure long-term beneficial AI), with other social responses such as doing nothing or banning being seen as impractical, and approaches such as enhancing human capabilities through transhumanism techniques like brain-computer interfaces being seen as potentially complementary. Regulation of research into artificial general intelligence (AGI) focuses on the role of review boards, from university or corporation to international levels, and on encouraging research into AI safety, together with the possibility of differential intellectual progress (prioritizing protective strategies over risky strategies in AI development) or conducting international mass surveillance to perform AGI arms control. For instance, the 'AGI Nanny' is a proposed strategy, potentially under the control of humanity, for preventing the creation of a dangerous superintelligence as well as for addressing other major threats to human well-being, such as subversion of the global financial system, until a true superintelligence can be safely created. It entails the creation of a smarter-than-human, but not superintelligent, AGI system connected to a large surveillance network, with the goal of monitoring humanity and protecting it from danger. Regulation of conscious, ethically aware AGIs focuses on how to integrate them with existing human society and can be divided into considerations of their legal standing and of their moral rights. Regulation of AI has been seen as restrictive, with a risk of preventing the development of AGI.
Agentic AI regulation The increased development and deployment of AI agents has shifted regulation discussion beyond generative AI. Agentic AI regulation is in early stages compared to generative AI systems, which have already had many regulations implemented or proposed. AI agents are more complex, more autonomous, and have less human oversight.
Kathrin Gardhouse et al. propose the idea of the many-hands problem through the lens of the EU Artificial Intelligence Act. Standard generative AI systems can be evaluated at the deployment stage. Then they can receive human oversight for each action as a single entity. AI agents, on the other hand, can work with multiple actors within multiple systems. This makes regulating their actions more difficult, especially when AI agents are compounded. Gardhouse et al. argue that AI agents have too many dynamic variables to have specific regulations based on fixed points in time. This may require more focus on system environments, record-keeping, and the agents themselves.
AI agents, by design, function with more limited human oversight. Recent articles, such as Governing AI Agents, convey doubts over whether regulatory frameworks for generative AI can effectively solve regulatory issues for AI agents. One approach is to have other deep learning systems or AI agents specifically monitor subsequent AI agents as an oversight measure. However, researchers maintain concerns that those could lead to the same types of vulnerabilities.
On June 29, 2026, United States Senator Mark Warner put forward a discussion draft for the Artificial Intelligence Access, Gatekeeper Exchange, and Nondiscriminatory Transfer Act (AI AGENT Act). This proposed discussion draft is geared toward a federal framework for consumer-facing AI agents. Access to large platforms, privacy, and security are the main concerns.
Regulation of fully autonomous weapons
Under the Convention on Certain Conventional Weapons, states have held discussions since 2014 on possible international rules for lethal autonomous weapon systems, an important area of AI regulation in warfare. Notably, informal meetings of experts took place in 2014, 2015 and 2016 and a Group of Governmental Experts (GGE) was appointed to further deliberate on the issue in 2016. A set of guiding principles on LAWS affirmed by the GGE on LAWS were adopted in 2018. The discussions have addressed international humanitarian law, accountability, possible prohibitions and regulations, and the extent of human control required over AI-enabled weapons.