Digital labor – Data Big and Small

Unboxing AI conference

I’m excited to be part of the organizing team for an upcoming conference entitled “Unboxing AI” and aiming to open – at least to an extent – the black box. What are the material conditions of AI production? Who are the multitudes of precarious workers who contribute to it in the shadow, by generating data and checking algorithmic outputs? What are the geographical areas and the social scope of the work that produces today’s intelligent technologies? These are some of the questions we aim to explore.

The first two days of the conference (November 5 and 6, 3pm – 7pm CET) will bring together highly regarded international specialists from a wide variety of disciplines (sociology, law, economics, but also the arts and humanities…). On the third day (November 7, also 3 pm – 7 pm CET), there will be a doctoral colloquium with a selection of very promising work by young researchers.

The conference was initially planned to take place in Milan in March 2020, and had to be postponed due to the Covid-19 pandemic. As the health situation is still critical, we have opted for an online-only version. At least, this format is cheap – no need to travel to attend – and we can welcome a more geographically diverse range of participants. Indeed the afternoon-only schedule is meant to enable colleagues from North and South America to attend.

Participation is free of charge but prior registration is required. You will find the programme as well as registration forms here
(please note that there is a separate form for each of the three dates of the conference).

The conference is organized as part of the initiatives of our ‘International Network on Digital Labor‘ and is co-sponsored by ISRF (Independent Social Research Foundation), the Nexa Center for Internet and Society, and Fondazione Feltrinelli.

Digital inequalities in time of pandemic

Just published a new, collective paper on new kinds of risk that are emerging with the COVID-19 virus, arguing that these risks are unequally distributed. Digital inequalities and social inequalities are rendering certain subgroups significantly more vulnerable to exposure to COVID-19. Populations bearing disproportionate risks include the social isolated, older adults, penal system subjects, digitally disadvantaged students, gig workers, and last-mile workers. We map out the intersection between COVID-19 risk factors and digital inequalities on each of these populations in order to examine how the digitally resourced have additional tools to mitigate some of the risks associated with the pandemic. We shed light on how the ongoing pandemic is deepening key axes of social differentiation, which were previously occluded from view.

These newly manifested forms of social differentiation can be conceived along several related dimensions. At their most general and abstract, these risks have to do with the capacity individuals have to control the risk of pathogen exposure. In order to fully manage exposure risk, individuals must control their physical environment to the greatest extent possible in order to prevent contact with potentially compromised physical spaces. In addition, they must control their social interactional environment to the greatest extent possible in order to minimize their contacts with potentially infected individuals. All else equal, those individuals who exercise more control over their exposure risk — on the basis of their control over their physical and social interactional environments — stand a better chance of staying healthy than those individuals who cannot manage exposure risk. Individuals therefore vary in terms of what we call their COVID-19 exposure risk profile (CERPs).

CERPs hinge on pre-existing forms of social differentiation such as socioeconomic status, as individuals with more economic resources at their disposal can better insulate themselves from exposure risk. Alongside socioeconomic status, one of the key forms of social differentiation connected with CERPs is digital (dis)advantage. Ceteris paribus, individuals who can more effectively digitize key parts of their lives enjoy better CERPs than individuals who cannot digitize these life realms. Therefore we believe that digital inequalities are directly and increasingly related to both life-or-death exposure to COVID-19, as well as excess deaths attributable to the larger conditions generated by the pandemic.

The article has been published in First Monday and is available here.

In the same special issue of First Monday, I co-published two reference articles:

Digital inequalities 2.0: Legacy inequalities in the information age

Digital inequalities 3.0: Emergent inequalities in the information age

New ANR Project HUSH: Human supply chain behind smart technologies

Together with sociologist Antonio A. Casilli and economist Ulrich Laitenberger, I have recently received ANR (French National Research Agency) funding for a new study of human inputs – mostly platform-mediated work in the production of artificial intelligence solutions. In our project called HUSH (Human supply chain behind smart technologies) we aim to shed light on the whole ecosystem linking platforms, workers and their clients demanding data-related and algorithmic services.

For this project, we are now looking for a

PhD researcher in digital economics

The position provides the opportunity to focus strongly on research, in a very active environment. The team has collaborations with different online platforms and has collected data sets from the web, which can be used by the applicant for their thesis. The focus of the current position is to work on the economic aspects of platform-mediated work, using quantitative analyses. Two other PhD students (in sociology) have already been recruited for this project and work on related topics.

The starting date is January 2020 (a later starting date is also possible). As per national regulations, the annual stipend will be about 1,600 euros per month, with possibility to obtain a complement for extra activities such as teaching. Social security and professional training are provided. Additional funding is available to present your research at international conferences and workshops. The position will be based at the new campus of Telecom Paris in Palaiseau, in the direct neighborhood of École Polytechnique and ENSAE.

Your profile

Applicants should have successfully completed a Master’s degree in economics, socio/economic data science or related disciplines, or expect completion at the beginning of the year 2020. They should have a strong interest in digital platforms, from the perspective of industrial organization or labor economics, and have an empirical focus (econometrics, data science). They should aim at developing programming skills and have an interest in the evaluation of internet data. Fluency in English is required; knowledge of French is advantageous, but not essential.

Telecom Paris and IP Paris

Telecom Paris is part of the newly founded Institute Polytechnique (IP) Paris, together with Ecole Polytechnique, ENSTA, ENSAE and Telecom Sud. The department of social sciences and economics (SES) at Telecom Paris studies the impact of the digitization on economic activity and society. For more information, please see https://www.telecom-paris.fr/fr/lecole/departements-enseignement-recherche/sciences-economiques-sociales/structure/economie-gestion

How to apply

Please submit a cover letter, a curriculum vitae, a transcript of records (listing all subjects taken and their grades), and contact details of one to two referees by November 15, 2019 to Ulrich Laitenberger ( laitenberger@enst.fr ).

Update: applications open until December 15, 2019.

Back from Reshaping Work 2018

I was last week at the second Reshaping Work in the Platform Economy in Amsterdam. The interest of this small conference is tht it brings together different actors of the platform economy, from academics and students to policymakers, union leaders, workers, and representatives of platforms to discuss.

In an overview of preliminary results of our project DiPLab, Antonio A. Casilli and I presented our reflection on how micro-work powers artificial intelligence (AI), in three main ways:

Training AI
Validating outcomes of AI
Impersonating AI when it is cheaper or simpler that real AI

No more details for now… it will come out as a working paper very soon!

Big data, big money: how companies thrive on informational resources

Information oils the economy – as we know since the path-breaking research of George Akerlof, Michael Spence and Joseph Stiglitz in the 1970s – and information can be extracted from data. Today, increased availability of “big” data creates the opportunity to access ever more information – for the good of the economy, then.

But in practice, how do companies extract value from this increasingly available information? In a nutshell, there are three ways in which they can do so: matching, targeted advertising, and market segmentation.

Matching is the key business idea of many recently-created companies and start-ups, and consists in helping potential parties to a transaction to find each other: driver and passenger (Uber), host and guest (Airbnb), buyer and seller (eBay), and so on. It is by processing users’ data with suitable algorithms that matching can be done, and the more detailed are the data, the more satisfactory the matching. Firms’ business model is usually based on taking a fee for each successful transaction (each realized match).

Targeted advertising is the practice of selecting, for each user, only the ads that correspond at best to their tastes or practices. Publicizing diapers to the general population will be largely ineffective as many people do not have young children; but targeting only those with young children is likely to produce better results. Here, the function of data is to help decide what to advertise to whom; useful data are people’s socio-demographic situation (age, marriage, children…), their current or past practices (if you bought diapers last week, you might do that again next week), and any declared tastes (for example as a post on Facebook or Twitter). How this produces a gain is obvious: if targeted adverts are more effective, sales will go up.

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Are we all data laborers?

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”.

Four eco-systems

Specialist Antonio Casilli distinguishes four forms of digital labor in platforms, and I am now going to briefly outline them.

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Discussing platform cooperativism

On Monday, 7 December 2015 at Telecom ParisTech, I was discussant at a seminar by New School scholar Trebor Scholz on “Unpacking Platform Cooperativism“.

Internet platforms carry an unprecedented potential of value creation, exploiting the extraordinary power of data and algorithms to extract and distribute information to an extent never seen before. Information, we know since Hayek’s times, is the fuel that keeps markets going, that eliminates “lemons” and ensures an ever-better coordination between buyers and sellers, borrowers and lenders, or landlords and tenants. At the same time, the internet has channeled the dream of a viable non-market society, since Rheingold’s 1993 revival of the “community” and Barbrook’s 1998 “hi-tech gift economy“. So, can we put this informational efficiency to the service of a more humane economy, based on relationships, solidarity and reciprocation, rather than on the sheer market system?

The so-called “sharing economy” suggests answers, but also displays a tension: the efforts of myriad grassroots associations to develop collaboration as a value and a practice, sharply contrasts the spectacular growth of firms like Airbnb and Uber, now large multinationals, and their alleged cavalier attitude to anti-trust regulations and workers’ rights. If some say Uber is not really about sharing and collaboration, it is difficult to draw the line.

This ambiguity is fostered by a public discourse that focuses on the sharing of assets – the spare room in your home, or a sit in your car – that digital platforms enable. Asset-sharing has economic and social appeal: it increases efficiency by preventing assets from lying idle, while reducing waste, shifting emphasis away from consumerist values (“access is better than ownership“), and facilitating sociality beyond mere consumption.

But it is often forgotten that asset-sharing does not produce value by itself: it involves extra labour. In economic jargon, capital and labour and complementary production factors. In practice, if you want to put your spare room on Airbnb, you must produce an ad, monitor your message inbox and reply swiftly. You must clean the room and do the laundry before and after a guest’s visit. You must show your guests around when they arrive.

More importantly, the very opportunity of asset-sharing changes the incentives that shape labour supply – people’s willingness to sell their time and effort against a payment. Because of the expected compensation, some people will renounce use of a (non-spare) room to accommodate visitors instead, and others will do more journeys to drive passengers around – so it’s not really about sharing unused assets, it is about self-employment and starting a micro-business. A work opportunity as a complement to (and sometimes a substitute for) a main job.

This is where debates on internet platforms and the sharing economy rejoin the growing literature on digital labour — and where the contribution of Trebor Scholz is illuminating. Where others see assets (ie, capital), he sees labour. He shows us that the bottlenecks here are about labour, not capital, and that the success — be it economic or social– of the sharing economy is closely tied to the destiny of labour. Whether it appears on the surface as self-employment, micro-entrepreneurship or salaried work, doesn’t really matter. Trebor reminds us of Marx’s fundamental principle that production relations are central to our (capitalist) society, and value generation rests ultimately on labor. If this very crucial part of the human experience goes wrong, even the best side of the sharing economy – the one that endorses trust, reciprocity, and zero-waste – may fail to perform any transformative effects on society.

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