Markets – Data Big and Small

Microwork platforms: a challenge for artificial intelligence, a challenge for employment?

With our sponsors France Stratégie and MSH Paris-Saclay, we convene an international conference on micro-work in Paris on June 13, 2019, followed by the first INDL (International Network on Digital Labor) workshop on June 14. The event will include a “meet the microworkers” panel on June 13, where workers, platform owners and client companies will take the stage. There will also be presentations of the results of national and international surveys (notably ours, DiPLab) on these emerging forms of work, and discussions with French and international academic and institutional experts.

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After Uber, Deliveroo and other on-demand services, micro-work is a new form of labor mediated by digital platforms. Internet and mobile services recruit crowds to perform small, standardized and repetitive tasks on behalf of corporate clients, in return for fees ranging from few cents to few euros. These tasks generally require low skills: taking a picture in a store, recognizing and classifying images, transcribing bits of text, formatting an electronic file… Despite their apparent simplicity, these micro-tasks performed by millions of people around the world, are crucial to create the databases needed to calibrate and “train” artificial intelligence algorithms.

Internationally, Amazon Mechanical Turk is the most widely known micro-work platform. In France and in French-speaking Africa, other platforms are attracting a growing number of workers to supplement or even substitute for their primary income. How widespread is the phenomenon? How to recognize, organize and regulate this new form of work? How, finally, does it relate to traditional forms of employment?

Presentations and discussions are held in French and English, with simultaneous translation.

The programme is available here.

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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Big Data redefine what “markets” are

The growth of “big data” changes the very essence of modern markets in an important sense. Big data are nothing but the digital traces of a growing number of people’s daily transactions, activities and movements, which are automatically recorded by digital devices and end up in huge amounts in the hands of companies and governments. Payments by debit and credit cards record timing, place, amount, and identity of payer and payee; supermarket loyalty cards report purchases by type, quantity, price, date; frequent traveler programs and public transport cards log users’ locations and movements; and CCTV cameras in retail centers, buses and urban streets capture details from clothing and gestures to facial expressions.

This means that all our market transactions – purchases and sales – are identifiable, and our card providers know a great deal about our economic actions. Our consumption habits (and income and tastes) may seem more opaque to scrutiny but at least to some extent, can be inferred from our locations, movements, and detail of expenses. If I buy some beer, maybe my supermarket cannot tell much about my drinking; but if I never buy any alcohol, it will have strong reasons to conclude that I am unlikely to get drunk. As data crunching techniques progress (admittedly, they are still in their infancy now), my supermarket will get better and better at gauging my habits, practices and preferences.

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