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Machine learning, social learning and the governance of self-driving cars - Jack Stilgoe,
Exception and Alert Notification. Organizational Hierarchy. Role-based Access Control. Map View. Extended Data Storage. Data Integration. Fuel and Carbon Management. Vehicle Diagnostics Fault Codes. Posted Speed Violations. Driver Safety Scorecards and Consoles. It is just waiting for its software and the regulators to catch up. Other driverless innovators are unconvinced that such hardware is up to the job.
While Tesla relies on ultrasonic sensors for short distances and cameras and radar for detecting objects further away, other companies have invested heavily in LiDAR — a laser-based step-up from radar. LiDAR has a longer range than ultrasound and is better than radar at spotting small objects made from a wide range of materials. But the technology is, as of , prohibitively expensive and bulky for a private car. Given these contingencies, policymakers have a more active part to play in the development of approaches, the setting of standards and the integration of technology into the built environment.
As I have described, autonomous vehicles are not as heroically independent as their enthusiasts would have us believe, nor are they as autodidactic. It is a story that deserves to be challenged. The emergence of self-driving cars will be a process of social learning that can and should be democratized. Much of the noisiest excitement surrounding self-driving cars has come from a culture of innovation that has little experience of the material, non-digital world and is unused to intense regulation. Large though still young Silicon Valley companies such as Tesla and Google have been joined by start-ups like Comma.
Car manufacturers with long histories that have come to accommodate and in some cases define substantial government oversight have, through acquisitions and partnerships, sought to take advantage of these new possibilities. This clash of hardware and software cultures raises immediate questions of governance. For example, these two worlds understand product liability very differently. Cars are conventionally designed, tested, and released as finished products with an ever-present threat of product recalls, fines or civil law suits if they are deemed defective.
Software, however, is governed in most jurisdictions as a service rather than a product, and granted substantial leeway Chander, The rapid uptake of machine learning looks set to exacerbate the irresponsibility that Nissenbaum feared. As the stakes of software deployment rise — in online security, social media and robotics — we may well see self-driving cars as a test case for the hardening of software regulation.
JafariNaimi argues that the self-driving car presents an opportunity for reframing transport governance. With the automobile in the 20 th century, a strong idea about what a car was — an everyday object like a bicycle rather than a sociotechnical system like a train and its tracks — led to regulatory regimes that concentrated power with cars, their drivers and manufacturers.
As with other emerging technologies Rayner, , makers of self-driving cars see their unarguable potential being held back by lags and deficits — in infrastructure, law and public understanding. Rather than taking the technology as fixed and looking to plug the deficits of law or public understanding that are imagined around it, policymakers should instead see self-driving cars as an opportunity for more active engagement in the shaping of technological systems, prioritizing social learning and knitting self-driving cars back into their social worlds.
The introduction of self-driving cars, with all the missteps and misadventures that will occur as they mix with other modes of transport, will represent an expansion of what is already a form of disorganized social experimentation.
Higher premiums versus privacy
Good governance will mean resisting the privatization of learning that is happening. It will mean engaging not just with technological outcomes, which, given the complexity of transport systems, will be radically indeterminate, but also with the processes and purposes — inscribed and implicit — of innovation. As I describe above, there are clear tensions between social learning and the pure form of machine learning manifest in deep neural networks. The opacity of machine learning systems, both deliberate and accidental, offers an excuse for innovators and a barrier for governance.
However, a closer look at self-driving car innovation reveals some constructive alternatives.
Engineers have already had to engage in a form of socialized machine learning, building on research in social robotics e. Algorithmic efficacy has attracted substantial attention, but it will be only part of the innovation required to make self-driving cars work. Engineering efforts to improve the interpretability of deep learning systems challenge the narrative of inevitable opacity that has until recently provided an easy excuse for irresponsibility. While some have argued that AVs should be allowed to travel unlabelled, so that other road users do not take advantage of their presumed generosity, there is growing recognition among engineers of the need for vehicles to actively communicate their presence, their intentions and their capabilities to other road users Surden and Williams, The politics of novelty surrounding self-driving cars is unpredictable.
However, if the whole system is to be as transformative as is claimed, its novelty should not be defined merely by technical advances in algorithms. Socializing machine learning demands the closer integration of insights from human-computer interaction and collaborative design into engineering rather than a presumption that a self-driving car merely means replacing a person with a computer. Most corporate and regulatory statements on self-driving have overlooked the contingencies of machine learning to focus on human deficiencies.
This hubris is likely to lead to a model of accidental governance in which car manufacturers set the terms of experimentation, and events such as crashes come to define, in the minds of publics and regulators, the trajectory of technology. This means reimagining public participation not as education, but as democracy. An important entry point for governments into the process of learning and experimentation with self-driving cars is through the sharing of data.
A self-driving car can already generate a gigabyte of data each second. The investigations of the Tesla crash provide a window into the politics of data sharing. Some car companies are starting to emphasize accountability. The reasons for doing so go beyond the investigation of accidents. Data is the fuel for machine learning and it is a source of competitive advantage for car companies. It is impossible to predict precisely how data will be monetized, because of the wide range of possibilities of future transport systems, but we can anticipate that aggregated or personalized data relating to geography, driving behaviours, traffic, people flow and more will become an important currency for future innovation.
The economies of scale will be substantial, tending towards new concentrations of economic power. Once we reject the narrative of autonomy and recognize the thicket of connections between cars and the outside world, we can imagine new possibilities for machine learning in the service of social learning.
If the development of self-driving algorithms is to realize some of the public value that its developers suggest, then there is a strong case for collaboration rather than competition. If cars learn more effectively as fleets, then it is reasonable to expect responsible car companies to share their learning with others. However, if algorithms and the data that feed them are imagined to be, as seems likely, a source of competitive advantage, then the public value of self-driving technology will be diminished.
Some tentative governance proposals in the US have urged greater data-sharing.
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The initial policy focus, as with local governance measures, is on safety. The disengagement reports submitted by companies reveal the gap between informal experimentation and formal compliance. Tesla claims that by the end of its customers had already covered more than a billion miles in Autopilot mode. What counts as a disengagement is largely left to the companies to decide. But, if nothing else, such reports begin to organize social learning from self-driving car experimentation. As it stands, much of the NHTSA guidance is voluntary, albeit with a thinly veiled threat of pre-market approval and proactive regulation if car companies misbehave.
Some companies have already sought to demonstrate their responsibility in data sharing in order to head off top down controls.
Uber, for example, has volunteered aggregate data on ride sharing for the benefit of transport planning. Industry representatives have responded that self-driving car data will be commercially valuable and must therefore be proprietary, except in situations where safety is a priority Hawkins, Even if sufficient data is forthcoming in the event of crashes, this will be only a small part of a much larger process of social learning. US leadership in self-driving car innovation has meant the inheritance of a mode of governance in which, among other characteristics, cars are seen as self-evidently beneficial, risks are governed retrospectively often through the courts , concerns about liberty are relatively elevated over those of public safety and public transport receives little support.
The default has been to govern self-driving cars according to this framework, defining risks narrowly while emphasizing the need to attract investment and create new markets.
Other countries have adopted a modulated form of this technology-first approach. However, there are notable examples of policies that, by starting with a focus on transport rather than technology, reframe the challenge. On the narrow issue of road safety, for example, Vision Zero, a strategy for reducing road deaths that began in Sweden, offers an alternative model of social learning that puts car innovation alongside infrastructure, law and social norms in redistributing responsibility for safety Eriksson, ; JafariNaimi, A social learning approach to governing self-driving cars would be similarly well rounded, putting the promise of machine learning in its place.
This paper was written during a sabbatical year at the University of Colorado, Boulder. Thank you to these audiences for helping to sharpen the ideas.