@article {2018,
	title = {Machine learning, social learning and the governance of self-driving cars},
	journal = {Social Studies of Science},
	volume = {48},
	year = {2018},
	pages = {25-56},
	abstract = {Self-driving cars, a quintessentially {\textquoteleft}smart{\textquoteright} 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 {\textquoteleft}Who is learning, what are they learning and how are they learning?{\textquoteright} 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. {\textquoteleft}Self-driving{\textquoteright} or {\textquoteleft}autonomous{\textquoteright} 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.},
	doi = {10.1177/0306312717741687},
	author = {Jack Stilgoe}
}
@article {2017,
	title = {Seeing Like a Tesla: How Can We Anticipate Self-Driving Worlds?},
	journal = {Glocalism},
	volume = {3},
	year = {2017},
	abstract = {In the last five years, investment and innovation in self-driving cars has accelerated dramatically. Automotive autonomy, once seen as impossible, is now sold as inevitable. Much of the governance discussion has centred on risk: will the cars be safer than their human-controlled counterparts? As with conventional cars, harder long-term questions relate to the future worlds that self-driving technologies might enable or even demand. The vision of an autonomous vehicle {\textendash} able to navigate the world{\textquoteright}s complexity using only its sensors and processors {\textendash} on offer from companies like Tesla is intentionally misleading. So-called {\textquotedblleft}autonomous{\textquotedblright} vehicles will depend upon webs of social and technical connectivity. For their purported benefits to be realised, infrastructures that were designed around humans will need to be upgraded in order to become machine-readable. It is vital to anticipate the politics of self-driving worlds in order to avoid exacerbating the inequalities that have emerged around conventional cars. Rather than being dazzled by the Tesla view, policymakers should start seeing like a city, from multiple perspectives. Good governance for self-driving cars means democratising experimentation and creating genuine collaboration between companies and local governments.

 },
	keywords = {automotive autonomy, governance, risk, self-driving cars, Tesla},
	doi = {10.12893/gjcpi.2017.3.2},
	author = {Jack Stilgoe}
}
