I gave today a talk at AUTONOMY, a major festival of urban mobility in Paris, where new technologies are at center stage, from driverless cars to electric scooters, bike-sharing solutions, and connected infrastructure for the smart city. I had been asked to talk about labor in digital platforms, such as those offering mobility services.
Digital platforms are often thought of in terms of automation, but it is clear that there is labor too: we all have in mind the example of the couriers and drivers of the “on-demand” economy. But there’s more: I’ll show how platforms involve the labor of everyone, including passengers and users of all types. By labor, I mean here human activity that produces data and information – the key source of value for platforms. It is often an implicit, invisible activity of which we may not even be aware – as we tend to focus more on consumption aspects as we talk routinely about “car pooling” or “car sharing”, rather than looking at the underlying productive effort. This is what scholars call “digital labor”.
The On-Demand Economy
A first ecosystem is that of drivers, couriers and other service providers of the on-demand economy. It is clear that theirs is work, and that it is paid, although generally it is freelance or self-employment rather than wage labor.
But it’s not all about the material work of driving and delivering: it is also about curating one’s online profile and maintaining a good reputational score, without which it might become difficult to obtain tasks or clients – and at worst, one risks being “deactivated” (to use Uber’s language). This behind-the-scenes activity is what sociologist Michel Callon calls “valuation” or “qualification” work: it is not remunerated per se and may even not be recognized explicitly as labor, but it is necessary to be part of the system, as a precondition to gain access to remunerated activities. Interestingly, it is an activity that concerns not only drivers but also passengers, as those with insufficient information on their profiles or low reputation may not appear trustful – and may struggle to find the services they need (say, get a car to drive them home at 3am in the morning). Valuation work is a form of implicit, immaterial work that concerns all platform users regardless of their roles, and which is unlikely to disappear with automation – because it is unrecognized and unpaid, it costs peanuts to platforms, who then have no reason to displace it.
The second ecosystem includes micro-work platforms. These are services that crowdsource very small, simple tasks to a multitude of remote workers, who receive micropayments in exchange. The most famous of them is Amazon Mechanical Turk, but there are many others such as Upwork. What can be generally easily crowdsourced are low-skilled tasks that are easy for humans to perform, but that an artificial intelligence cannot currently perform very well (or needs to be trained beforehand). These tasks may consist, for example, in translating short bits of text, recognizing or tagging images, ordering playlists of videos or music. An example of task that is particularly relevant for the sector of mobility is the testing of software applications, mobile tools and user interfaces – the basis of the operations of any digital platform.
There are two things to stress here: first, these tasks are ultimately related to data – the fuel of the platform economy – in that text of image recognition, tagging, translating and the like, are all ways to assist collection of new data or curation of existing datasets. Second, these tasks are paid and it is patent that doing them is work –regardless of the legal status of workers and of the small size (often below minimum wage) of payments.
The third ecosystem includes the invisible work of users of social platforms, from generalist ones such as Facebook, Google or Twitter, to more specialized ones. To an extent, users do just the same activities as the micro-workers mentioned above: likes, shares, creation of playlist, or organization of content. But contrary to micro-workers, social media users do not perform these tasks explicitly for a third party, and do not necessarily see them as labor: on the contrary, they might consider the texts they post on their blogs, or the photos they publish on Instagram, as creative and self-fulfilling activities that they do for their own pleasure, interest and satisfaction. Likewise, they do not usually expect to be paid. Yet from the viewpoint of the platform, more and less creative activities contribute to the same ultimate goal: feeding into the platform’s databases and allowing it to sell better-targeted advertising spaces to its clients. That is, this is again human activity that enriches data – and contributes to boosting the platform’s profits. It is digital labor, again – although it often fails to be recognised as such, and it is not normally paid.
In short, the offer of services (including mobility services) by major digital platforms is based on their use of our data, sometimes in non-transparent ways. Because these services (Google Maps for example) are often offered at no fee, users have often (more or less knowingly) accepted to release their data in exchange. But the conditions under which major digital platforms capture the value of these data is coming to the surface today, and raises the question of whether it is really a win-win situation as claimed by platforms. Aren’t users bound to lose, instead? Recent lawsuits against some giants of the web (for example, through the “Europe vs Facebook” association) and the growing interest of regulators both in Europe and in USA have put these issues on the table.
The fourth ecosystem refers to the Internet of Things and the data set that connected objects produce. This is particularly relevant in the sector of mobility, with the incessant progress of connectivity and automation (driverless cars for example). Again, we see here human actions that produce data through use of connected objects – even if the “workers” are not always aware of being involved in a productive activity, and are usually not paid. However, there are notable exceptions, such as the Bitwalking application, which pays users (in bitcoins) to walk, recognizing that their geolocalized movements produce data with high commercial value.
Blurring the boundaries between work, leisure and rest
A first consequence of these transformations is the blurring of the boundaries previously the activities of work, recreation and rest, which were previously distinct. Supposedly for-leisure applications produce value just as answering professional emails does. Researchers have proposed new concepts to define this situation, such as Weisure (work + leisure, Dalton Conley), Playbor (= labor + play, Julian Kücklich), and Hyperemployment (Ian Bogost).
The public and private spheres get conflated
The connected home (just as the connected car or the connected bike) becomes a data factory, and in that sense, there is no more any clear distinction between living spaces and workplaces. This can be seen as part of a broader trend that confuses the private sphere with the public sphere – a phenomenon that has been observed since the rise of the rise of the social web about ten years ago.
In this world in which we use and produce data incessantly and sometimes unknowingly, a first next step is a new negotiation of privacy. The current transformations do not mean we must give up any attempt to protect our privacy (or personal data), and necessarily forget about any right we might have with respect to the economic value they can produce. Contrary to what some have claimed, and as I found in a study of a couple years ago, privacy is not dead: but its economic value, its social significance, and its moral sense have changed.
We must reinvent privacy by “negotiating” its perimeter and constantly adapting to today’s changing context. Negotiation is key to a fairer appropriation of the value of data, and a fairer governance of the digital platforms of future mobility solutions.