The current global AI governance landscape displays a multitude of governance initiatives by various actors, some dealing with the regulation of very specific AI applications and others with more general, abstract principles of AI ethics and policy. By now, many countries have brought forward their own AI strategies, often with direct reference to the international level and questions of global AI governance. While these alone are valuable objects of study, this article focuses on those actors and initiatives that are by nature transnational or multilateral, i.e., that involve stakeholders from more than two countries. At this stage, almost none of them entail binding legislation, but rather political declarations, ethical principles, or partnerships.

There are many ways to organize these actors and initiatives, structuring them by their regional or topical scope, by the actors’ nature (e.g., governmental, business, civil society) or by the kind of instrument involved (e.g., international treaty or organization, alliances or partnerships, political declarations).

The article employs a two-by-two matrix (Table below) that distinguishes the following characteristics: (a) between action that is embedded in the existing governance architecture vs action that establishes new instruments; and (b) between state-led initiatives and non-state-led initiatives. Note that the latter dimension considers the origin or agency of action, not necessarily the organizational nature through which it is ultimately carried out.

With all this in mind, the article argues that we are witnessing the first signs of consolidation in this fragmented landscape. The nascent AI regime that emerges is polycentric and fragmented but gravitates around the OECD, which holds considerable epistemic authority and norm-setting power. It is polycentric because it features different epistemic communities and multiple centers of decision-making, each operating with some degree of autonomy. It is fragmented because there is substantial overlap in different actors’ membership and the topics addressed by these initiatives; the well-connected epistemic communities are equally overlapping. As with other polycentric governance architectures, global AI governance will likely continue to struggle with the challenge of coordination. While epistemic and membership overlap may benefit consolidation or convergence, topic overlap tends to foster fragmentation and adds complexity to the regime.

The article is mostly agnostic to the content of what these global governance initiatives and arrangements actually entail. This was a deliberate choice to focus the analysis on structure, actors and instruments, to avoid confusion between structure and content. Nevertheless, a quick look at the main developments suggests that there is convergence on a certain type of AI values and principles, as put forward by the European Commission and the OECD. These are focusing on trustworthy, human-centric AI.

Such terms are of course abstract and somewhat vague, thus leaving room for interpretation. This interpretation, contextualization, and operationalization of AI values will without doubt experience major contestation by different actors. While China is side-lined from most of the above initiatives, its role in AI governance cannot be understated. The government has signalled willingness to engage in global governance as a responsible actor, and specifically on AI ethics has made some steps towards conciliation. Yet, it will want to interpret AI ethics in accordance with its own cultural context and promote these views globally. Hence, how China engages with the GPAI and other governance initiatives (and vice-versa) will be an interesting space to watch and leaves ample room for future research.