Machine Learning, Social Learning and the Governance of Self-Driving Cars

Social Studies of Science, 2017

56 Pages Posted: 20 Mar 2017 Last revised: 6 Feb 2018

Jack Stilgoe

University College London

Date Written: March 19, 2017

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.

Keywords: self-driving cars, autonomous vehicles, responsible innovation, machine learning, social learning

Suggested Citation

Stilgoe, Jack, Machine Learning, Social Learning and the Governance of Self-Driving Cars (March 19, 2017). Social Studies of Science, 2017. Available at SSRN: https://ssrn.com/abstract=2937316 or http://dx.doi.org/10.2139/ssrn.2937316

Jack Stilgoe (Contact Author)

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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