Department of Science and Technology Studies, University College London, London, UK.
Soc Stud Sci. 2018 Feb;48(1):25-56. doi: 10.1177/0306312717741687. Epub 2017 Nov 21.
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.
自动驾驶汽车是一种典型的“智能”技术，但并非天生智能。控制其运动的算法是随着技术的出现而学习的。自动驾驶汽车是机器学习能力的高风险测试，也是技术治理中社会学习的一个案例。社会在了解这项技术的同时，技术也在了解社会。理解和治理这项技术的政治意味着要问“谁在学习，他们在学什么，他们是如何学习的？”我关注的是 2016 年特斯拉 Model S 备受瞩目的撞车事故周围社会学习的成败，我认为，交通领域机器学习的轨迹和修辞提出了重大的治理挑战。“自动驾驶”或“自主”汽车的命名有误。与其他技术一样，它们是由对社会需求、可解决问题和经济机会的假设塑造的。为了公众利益而治理这些技术，意味着要通过建设性地应对机器学习的偶然性来改善社会学习。
