Machine learning, social learning and the governance of self-driving cars

TLDR

Self‑driving cars are not inherently smart; they evolve through mutual learning between technology and society, shaped by assumptions about social needs, solvable problems, and economic opportunities. The paper investigates how self‑driving cars serve as a high‑stakes test of machine learning and social learning in governance, arguing that understanding who learns, what they learn, and how they learn—illustrated by the 2016 Tesla crash—reveals substantial governance challenges that require improved social learning through constructive engagement. The study focuses on the learning algorithms that control vehicle movements as the technology evolves. The authors conclude that the terms “self‑driving” or “autonomous” are misnomers.

Abstract

Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘Who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.