TPRC 2024: The 52nd Research Conference on Communications, Information and Inter

Link to paper

Abstract:
The boom in AI governance initiatives rests on deeply flawed understandings of digital technology and its underlying political economy. This paper rejects prevailing conceptualizations of the AI governance problem and goes so far as to reject the label “AI” as a meaningful and useful name for the object of governance. What we now call “AI” is really a globally integrated digital ecosystem composed of computing devices, digital networks, digitized data, and software programs. The article’s theme is that what we now call “artificial intelligence” is not a new technology that creates its own distinctive governance problems, but outgrowths of computing and the ecosystem of technical standards, data, devices and networks that have grown up around it. From a public policy standpoint, “AI” is an unscientific, over-simplified label for evolving applications of computing. The applications we call AI are so numerous, so diverse, and so indistinguishable from computing as to render the concept of “AI governance” meaningless.

The claim that AI doesn’t exist may seem tendentious and exaggerated, but it has the virtue of clearing the deck for a more accurate understanding of the governance implications of the digital transformation. Once we stop obsessing about “AI” and focus attention on the broader digital ecosystem, the governance problems we face are clarified. “Governing” the production and use of intelligent applications requires systemic awareness of nearly all manifestations of computing. In other words, what most people mean by “AI governance” presumes comprehensive data governance, controls on the production and distribution of semiconductors and other devices, effective Internet governance, regulation of cloud providers/platforms, and regulation of the production and distribution of software and software architectures. Further, the policy and governance problems allegedly caused by “AI” predate LLMs and chatbots and have cropped up repeatedly during the longer-term history of computing and the Internet. “AI governance” is just digital governance.

Shifting our focus to the digital ecosystem also facilitates a more realistic assessment of the necessity and proportionality of regulatory interventions. It enhances awareness of the economic and social costs of ecosystem-wide restrictions, particularly regarding freedom of expression, open competition in ICT products and services, and the ability to explore and innovate new applications of computing. Further, when it is clear that that the object of governance is the entire digital ecosystem and not some new, isolated thing called “AI,” we are in a much better position to assess what measures would be effective and how much governance is feasible in a world where heterogeneous technologies and distributed decision making are rampant, states compete for power, and no single state has supreme authority over the entire ecosystem.

The paper proceeds along the following lines. Part 1 provides a basic definition and description of the digital ecosystem and its components and explains why that conceptualization works better than various alternatives. Part 2 traces the scientific origins of the digital ecosystem and shows that cybernetic control and automation via artificial intelligence or machine learning were known to be latent in computing technology from the 1940s. Part 3 tracks the evolution of intelligent applications to show empirically how “AI” progress was tied to progressive improvement in the capabilities of all four components of the digital ecosystem, and that every one of the problems attributed to “AI” arose during the evolution of the Internet and other forms of computing. Hence, no clear line can be drawn between the governance of AI applications and the governance of the broader digital ecosystem. Part 4 evaluates some of the current proposals to “govern AI,” demonstrating they generally attempt to have the tail of AI applications wag the dog of the entire digital political economy, often resulting in ideas that either lack feasibility and/or entail extraordinary centralizations of power that could backfire on their proponents.