Article ID: CBB026908939

Machine learning, social learning and the governance of self-driving cars (February 2018)

unapi

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.

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Authors & Contributors
Asaro, Peter M.
Gunnarsdóttir, Kristrún
Jakle, John A.
Lamoreaux, Naomi R.
Mellström, Ulf
Sculle, Keith A.
Journals
IEEE Technology and Society Magazine
Transfers
Laboratorio dell'ISPF
The Bridge: Journal of the National Academy of Engineering
Minerva: A Review of Science, Learning and Policy
Science, Technology, and Human Values
Publishers
MIT Press
Springer
The MIT Press
University of Tennessee Press
Concepts
Technology and ethics
Artificial intelligence
Technology and society
Machine learning
Technological innovation
Autonomous vehicles
Time Periods
21st century
20th century, late
20th century
19th century
Places
United States
Europe
Institutions
International Business Machines Corporation
U.S., National Highway Traffic Safety Administration
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