Defining Public AI

AI technologies promise to affect nearly every aspect of our lives.¹1 Knowing that this transformation will play out on a societal scale requires governance and ownership mechanisms that entitle a plurality of voices to steer AI not simply as a new category of tech products, but as a public resource and infrastructure.²2 In democratic societies, the latter are subject to varying degrees of ‘publicness’ through (1) accessibility for use, (2) maintenance as a matter of public responsibility, and (3) accountability to the public in their function. Additionally, (4) participation in strategic decisions about the development and application of resources, and (5) how the value that is derived from these resources is distributed, which are typically harder to render public.³3

Within complex innovation systems both public and private investments flow into research and development. Instead of understanding ‘public’ and ‘private’ as binary conditions attached to specific types of intermediaries (i.e. state versus market actors), FAE proposes to regard the notion of publicness as a spectrum on which the terms of public agency are negotiated, ranging from ‘thin’ to ‘thick’. ‘Thick’ public governance might apply to state-run assets funded by taxes, while ‘thin’ publicness could be obtained when/where infrastructures are freely accessible to the public but are privately owned. Between these extremes, frameworks such as commoning could be deployed to pool resources offering greater access and maintenance buy-in.⁴4 ⁵5 Mission-driven public-private partnerships, such as those developed to build national supercomputing capabilities, leverage state-level accountability and strategic development while cooperating with private industry to deliver specialty products and services.⁶6 ⁷7 Non-governmental interest organisations, academia, and grassroots movements also operate within this ecosystem to address unmet needs, both at the level of general policy and on behalf of specifically affected communities.⁸8

Today’s AI is constituted through an entanglement of resources and infrastructures, each layer possessing its own context and openings for incorporating publicness into its design. What follows is the mapping of entanglements through AI’s technical stack, consisting of seven hierarchical layers organised in two tiers. The ‘hardware’ tier which provides the physical material and machinery by means of natural resource, server networks and compute layers. These enable the transfer and processing of information in the 'software', from the data layer, to the model, network protocols and application layers. By zooming in on the stack layers, the interdependence between industry, states, non-governmental organisations, academia, and the many publics that are implicated in the creation and adoption of AI can be understood. Highlighting the fact that ‘public AI’ is not just a speculative category, but a reality that requires ongoing development and support.⁹9

Applications in the context of this AI stack are software products that utilise machine learning models as a core part of their capability; for example, content creation services such as Stable Diffusion, ChatGPT, or Suno.ai. Other present-day applications include virtual assistants (e.g. Apple’s Siri), recommendation systems (e.g. those used by Netflix), developer tools for writing code (e.g. GitHub’s CoPilot), speech and language recognition tools (e.g. Google Translate), biometric identification technology (e.g. fingerprint recognition AppLock), computer sensing and simulation systems (e.g. as those operated by driverless vehicles such as CARLA), search engines (e.g. Google Search and Bing), and others.

While the release of ChatGPT in November 2022 captured the public’s imagination, the history of applications that use different underlying AI capabilities (or precursors to AI) spans decades.¹⁰10 Companies are, by default, incentivised to develop commercial products for consumers; however, governments, non-profits, and individuals also develop their own applications. For instance, in the UK the National Health Service develops applications for doctors to more accurately detect diseases using patient data.¹¹11 However, applications developed by the public sector are not always state-owned. Sometimes they are developed privately and licensed to the public sector. Regardless, privacy and consent are rights that are intrinsic to the individual irrespective of whether or not the tool is developed by the public sector, private sector, or state-owned.¹²12

Application development is underpinned by a robust ecosystem of open research and code sharing. The development of applications relies on platforms such as GitHub (for code), and HuggingFace (for machine learning models and datasets). Applications can be patented (e.g. Spotify). In the US and Europe, applications are subject to a voluntary code of practice, which extends to application providers in general.¹³13 ¹⁴14 Applications are also subject to a further round of scrutiny within digital marketplaces such as Apple’s App Store or Google Play.

Network protocols define the rules for how data is transmitted and received over a network, enabling AI applications to communicate efficiently with each other, with data sources, with AI models, and with end-users.

Hypertext Transfer Protocol (HTTP) is the open data communication protocol underlying the World Wide Web. Its specifications standardise the exchange of information; they are publicly available and can be used by anyone.¹⁵15 As a protocol, it is not regulated by any single governmental entity. Instead, it is maintained and developed to international standards organisations, primarily the Internet Engineering Task Force (IETF) and the World Wide Web Consortium (W3C). These organisations work through consensus-driven processes involving various stakeholders, including developers, engineers, industry representatives, and others, to ensure the protocol remains effective, interoperable, and up to date with evolving technological needs.¹⁶16

An Application Programming Interface (API), on the other hand, is a set of tools that enables the exchange of data and functionality between platforms, and integrations between different systems and devices. In AI applications, APIs are often used to access the capabilities of AI models, without interacting with the model itself, at a fee (e.g. enterprise-grade Gemini and ChatGPT Enterprise).

A machine learning model is a computer programme. In contrast to conventional programming, it is not manually defined through a sequence of instructions. Instead, the process of defining the computer programme is automated by means of algorithms that find patterns in large quantities of exemplary data.¹⁷17 While there are many different machine learning methods, deep learning algorithms are the current predominant subset of methods. They typically use deep artificial neural network architectures with multiple layers that can recognise features and context in data.¹⁸18 The model configuration is defined by a set of numerical parameters known as the model's weights. In 2021, the concept of a ‘foundation model’ was coined to describe a large (i.e. powerful) model that can process or generate information from multiple types of data inputs, such as text, images, audio, and video.¹⁹19 Today these foundation models are largely multimodal, meaning they can move across those types of input and output. Since training large models from scratch is expensive, requiring large amounts of data and compute resources, foundation models provide an opportunity for commercialisation. Examples of commercial foundation models include OpenAI’s GPT-4 and Google’s Gemini, while Meta’s Llama 2 and the Mistral models are open source.²⁰20

Building efforts have consolidated around just a few foundation models within corporate, or privately-controlled, contexts due to the massive investments that they require.²¹21 However, more recent projects have shown that smaller models are starting to compete with foundation ones.²²22 ²³23 Nevertheless, only a handful of foundation models from US-based companies have the greatest number of users globally. There is debate as to whether foundation models should be required to be open source due to their impacts as a ‘foundational layer’ of most AI applications - Meta’s Llama 2, Stability AI’s Stable Diffusion and Google’s BERT are examples of open source foundation models emerging from proprietary companies - or whether the models should be subject to regulation by governments in jurisdictions where they are in use (or some combination of both).²⁴24 ²⁵25

Whether open source or closed, not all model-makers reveal the contents of their training data, and it is believed that much of it is protected by copyright. Consequently, these leading AI organisations are confronting legal challenges that have yet to result in new laws.²⁶26 ²⁷27 ²⁸28 Meanwhile, the Responsible AI Licenses (RAIL) initiative is advocating for the development of licences for fine-tuning and downstream usage of models that curtails misuse.²⁹29

Models require high quality data for training and fine-tuning.³⁰30 While early efforts to create openly available, labelled datasets, for example ImageNet, improved the quality and capabilities of AI models, recent technical advances in foundation models have reduced the reliance on such resource-intensive efforts.³¹31 ³²32 Instead, the web is directly scraped by a large-scale web crawler, resulting in the Common Crawl dataset, which is a continually-updating set of raw webpage data with extracted metadata and text.³³33 Despite increasing criticism, it still serves as the most important resource for researchers, developers, and anyone interested in analysing the vast amount of information available on the internet.³⁴34

High profile lawsuits brought against companies including OpenAI, Microsoft, Stability AI, and Midjourney allege that their models infringe on copyrighted material in the training of their models.³⁵35 Even the use of openly accessible, or Creative Commons licensed material, has engendered significant debate around concerns relating to value extraction. Web crawling can also lead to datasets unintentionally containing illegal content. For example, the community-driven open source dataset LAION-5B has been accused of containing child abuse material.³⁶36 ³⁷37 Other open data repositories, for example libraries on HuggingFace, offer developers easy access to specific datasets, while vendors such as Google Ads are given consent by users to own their data and then monetise it. Governments and the healthcare industry have high-quality datasets related to societies’ overall level of wellness through medical imaging and health records, as well as census and municipal data.³⁸38 ³⁹39

Data-driven machine learning algorithms are reliant on high-performance computing. Graphics processing units (GPUs) are semiconductor chips originally designed for 3D graphics, but their competency in performing complex mathematical calculations at high speeds has made them hardware that is fundamental to AI systems in order for models to be trained quickly and at scale.⁴⁰40 Nvidia, Advanced Micro Devices (AMD) and Intel are currently the largest companies producing GPU hardware. Additionally, organisations that offer applications that use computational power do not pay Nvidia directly for GPUs. Instead, they access GPUs via cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform, which, in turn, buy chips in bulk from companies including Nvidia.

Because of high demand, intense global competition and geopolitical tensions have emerged surrounding the semiconductor industry; nations are vying for dominance in manufacturing, design, and supply chain control. In the past three years, particularly in response to Taiwan’s geopolitical status as the leading global chip producer, governments in the US, the EU, and the UK have pushed to support national semiconductor manufacturing, research, and development through new policy positions, legislation, and by providing financial incentives. Generally, investment by outside actors in national or enterprise computing projects is regarded now as a matter of national security and global competition.⁴¹41 Despite attempts by the US government to slow R&D capacity through restrictions on key exports in China and the Middle East, China is fast catching up to state-of-the-art chip manufacturing technologies. In addition to governments, industry players have also become active. OpenAI, for example, is seeking to raise $7tn, significantly from investors in the Arabian Gulf, for its own chip production capacity.⁴²42

As opposed to applications, network protocols, and small-scale models, the computing costs of foundational models means that GPUs are less accessible for small-scale entities or open innovation, raising questions about enclosure. It is a resource that has consolidated around a few key companies that service both the market and government needs.

Server networks, otherwise known as ‘clouds’, are clusters of computers that store data, run software such as AI models, and provide access to both data and models via APIs and communication protocols.⁴³43 To operate on the internet, server networks rely on the vast system of underwater cables which act as highways for data traffic across the planet. Over recent years, major server networks like Amazon Web Services and Microsoft Azure have come to account for much of the web. The business model of server networks is a straightforward exchange of use/access for a fee. Companies that rely heavily on server architecture – such as Google – build their own.⁴⁴44 The strategic placement of servers near where they will be needed most has instigated a land grab by companies and governments as they try to secure space to build new server racks and cooling systems. National security is increasingly a major concern for countries as governments allow foreign companies to operate servers in their jurisdiction. In Guizhou, China, for instance, Apple operates the Chinese iCloud, a server network that is not connected to the global Apple iCloud.

Control over natural resources including oil, gas, and coal has shaped modern society, creating massive levels of wealth, and establishing new regulatory regimes, while simultaneously accelerating environmental breakdown and laying the foundation for the technological developments of the 20^th and 21^st centuries. In centuries past, the search for and sequestration of natural resources provided the foundation of colonial projects. Today, control over rare earth metals, integral to both the global rollout of renewable energy and the mass expansion of AI, will define relationships between countries and companies who have access to rare metal wealth and those who do not.

The natural resources required for AI can be broadly grouped under the headings of materials and energy. Materials e.g. silicon, gold, silver, palladium, and lithium are necessary for the fabrication of chips, servers, cables, and batteries. Energy from renewable sources (wind, solar, hydro), fossil fuels (oil, gas, coal), and nuclear reactions (fission and fusion) power the data centres, server networks, and computing operations, as well as their cooling systems.

From publicly owned oil companies such as Norway’s Equinor, to fully privatised water companies in the UK such as Thames Water, governance of natural resources across the Western world varies by location and resource. Much attention in recent years has been focused on establishing democratically, or at least more publicly, accountable energy systems across Europe with an accompanying shift away from oil, gas and coal towards renewables.⁴⁵45 This process has been accelerated by the massive profits generated by gas suppliers following the price spike resulting from the attack on Ukraine, and by accelerating climate breakdown. Less attention has been focused on the supply chains, and often weak governance models in markets for rare earth metals such as palladium and lithium, or even more traditional metal commodities, such as gold and silver. Often, these metals, key to a transition to renewable energy, are extracted from the territories of the former colonies of Europe, as transnational corporations accrue massive profits from this resource extraction at the expense of their workers and of local communities.⁴⁶46