Stilgoe, JEZ;
(2018)
Machine learning, social learning and the governance of self-driving cars.
Social Studies of Science
, 48
(1)
pp. 25-56.
10.1177/0306312717741687.
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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.
Type: | Article |
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Title: | Machine learning, social learning and the governance of self-driving cars |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1177/0306312717741687 |
Publisher version: | http://doi.org/10.1177/0306312717741687 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | autonomous vehicles, machine learning, responsible innovation, self-driving cars, social learning |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Science and Technology Studies |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10038434 |
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