The means of prediction and the production function of AI
TL;DR
This brief argues that market-based governance fails: individual property rights over data cannot address AI’s harms and benefits, because machine learning is fundamentally about data externalities, and because platform network effects are artificially maintained.
Abstract
Who gets to decide what AI systems optimize for? Current debates frame the risks of AI as a conflict between humans and machines. This brief argues instead that the central conflicts are between different groups of people, over the choice of the objectives that AI systems are built to maximize. Control over these objectives rests with those who control the inputs to AI, that is, the means of prediction: data, compute, expertise, and energy. To shed light on this control, I discuss the production function of AI, which maps data and compute into predictive performance, drawing on statistical learning theory and on the empirical scaling laws that have driven the industry’s costly scramble for scale and the resulting concentration of power. I then argue that market-based governance fails: individual property rights over data cannot address AI’s harms and benefits, because machine learning is fundamentally about data externalities, and because platform network effects are artificially maintained. I conclude with proposals for democratic control of the means of prediction, through institutions such as sortition and liquid democracy, to give those affected by algorithmic decisions a say over the objectives that AI pursues.