Archive for the ‘ Data in the digital economy ’ Category

Recent ethical challenges in social network analysis (RECSNA17)

Research on social networks is experiencing unprecedented growth, fuelled by the consolidation of network science and the increasing availability of data from digital networking platforms. However, it raises formidable ethical issues that often fall outside existing regulations and guidelines. New tools to collect, treat, store personal data expose both researchers and participants to specific risks. Political use and business capture of scientific results transcend standard research concerns. Legal and social ramifications of studies on personal ties and human networks surface.

We invite contributions from researchers in the social sciences, economics, management, statistics, computer science, law and philosophy, as well as other stakeholders to advance the ethical reflection in the face of new research challenges.

The workshop will take place on 5 December 2017 (full day) at MSH Paris-Saclay, with open keynote sessions to be held on 6 December 2017 (morning) at Hôtel de Lauzun, a 17th century palace in the heart of historic Île de la Cité.

Calendar:

  • Submit a 300-word abstract by 15 October 2017.
  • Let us know if you wish to be panel discussant or session chair by 20 October 2017 (send to: recsna17@msh-paris-saclay.fr).
  • Acceptance notifications will be sent by 31 October 2017.
  • Registration is free but mandatory: speakers (and discussants and chairs) should register between 15 October and 15 November 2017, other attendees by 30 November 2017.

Keynote Speakers

José Luis Molina, Autonomous University of Barcelona, “HyperEthics: A Critical Account”
Bernie Hogan, Oxford Internet Institute, “Privatising the personal network: Ethical challenges for social network site research”

Scientific Committee

Antonio A. Casilli (Telecom ParisTech, FR), Alessio D’Angelo (Middlesex University, UK), Guillaume Favre (University of Toulouse Jean-Jaurès, FR), Bernie Hogan (Oxford Internet Institute, UK), Elise Penalva-Icher (University of Paris Dauphine, FR), Louise Ryan (University of Sheffield, UK), Paola Tubaro (CNRS, FR).

Contact us

Email: recsna17@msh-paris-saclay.fr
Webpage: http://recsna17.sciencesconf.org
Twitter: @recsna17

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Sharing Networks 2017: pen-and-paper fieldwork in a big data world

I’m excited to report that earlier this month, I ran the second wave of data collection for our Sharing Networks research project at OuiShare Fest 2017!

Publicizing the survey at OuiShare Fest 2017

To understand how people form and reinforce face-to-face network ties at such an event, I fielded a questionnaire with the help of a committed and effective team of co-researchers. It is a “name generator” asking respondents to name those they knew before the OuiShare Fest, and met again  there (“old frields”); and those they met during the event for the first time (“new contacts”). Participants then have to choose those among their “old” and “new” contacts, that they would like to contact again in future for joint projects or collaborations.

Interestingly, my good old pen-and-paper questionnaire still gives a lot of insight that digital data from social media cannot provide – just like a highly computer literate community such as this feels the need to meet physically in one place every year for a few days. Like trade fairs that flourish even more in the internet era, the OuiShare Fest gathers more participants at each edition. They meet in person there, which is why they are to be invited to respond in person too.

One part of the Sharing Networks 2017 onsite survey team.

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Networks in the collaborative economy: social ties at the OuiShare Fest 2016

The OuiShare Fest brings together representatives of the international collaborative economy community. One of its goals is to expose participants to inspiring new ideas, while also offering them an opportunity for networking and building collaborative ties.

At the 2016 OuiShare Fest, we ran a study of people’s networking. Attendees, speakers and team members were asked to complete a brief questionnaire, on paper or online.Through this questionnaire, we gained information on the relationships of 445 persons – about one-third of participants.

Ties that separate: the inheritance of past relationships

For many participants, the Fest was an opportunity to catch up with others they knew before. Of these relations, half are 12 months old at most. About 40% of them were formed at work; 15% at previous OuiShare Fests or other collaborative economy experiences; 9% can be ascribed to living in the same town or neighborhood; and 7% date back to school time.

Figure 1: pre-existing ties

Figure 1 is a synthesis of these “catching-up-with-old-friends” relationships, in the shape of a network where small black dots represent people and blue lines represent social ties between them. At the center of the graph are “isolates”, participants who had no pre-existing relationship among OuiShare Fest attendees. The remaining 60% have prior connections, but form part of separate clusters. Some of them (27%) form a rather large component, visible at the top of the figure, where each member is directly or indirectly connected to anyone else in that component. There are also two medium-sized clusters of connected people at the bottom. The rest consists of many tiny sub-groups, mostly of 2-3 individuals each.

Ties that bind: new acquaintances made at the event

Participants told us that they also met new persons at the Fest. Figure 2 enriches Figure 1 by adding – in red – the new connections that people made during the event. The ties formed during the Fest connect the clusters that were separate before: now, 86% of participants are in the largest network component, meaning that any one of them can reach, directly or indirectly, 86% of the others.

Figure 2: new ties created at the event

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Open Data: What’s new in 2017?

I am now in Montréal, where I participated, last Friday, in a panel on Open Data at “Science & You” international conference. It was interesting for me to reflect on how the picture has changed since my previous panel on the same topic – in Kiev in 2012. Back then, we were busy trying to convince public administrations that data opening was good for transparency and could help improve services to communities. Since then, a lot of attempts have been made in numerous countries – local authorities often pioneering the process, followed only later by central governments (one example cited in my panel was Québec City). What is made open is typically information from public registers (first names of newborns, records of road accidents) and increasingly, from technological devices and sensors (bus traffic information).

There are some conditions to be met for a dataset to be said “open”:

  • Technically, it needs to be “raw”, detailed, digital and reusable. The French Interior Ministry released results of the first round of the recent presidential elections within a few days, at polling station level. This is sufficiently detailed (with over 69,000 polling stations throughout the country), raw (allowing aggregations, comparisons etc.), and digital/reusable (so much so that the newspaper Le Monde could develop a user-friendly application to let readers easily check results in their neighborhoods). Some would also insist that “open” data should be released in non-proprietary formats (better .csv than .xls, for example).
  • Legally, the data must come with a license that allows re-use by third parties (typically within the Creative Commons family). Ideally, no type of reuse should be ruled out (including somewhat controversially, commercial / for-profit reuse).
  • Economically, the data should be available to all for free (or at least with minimal charges if data preparation requires extra work or expenses).

If in the past few years, a lot of thought has been devoted to the “ideal” conditions for data opening and how this would positively affect public service, the data landscape has now significantly changed.

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A cooperative approach to platforms

I was yesterday at a nice and interesting conference in Brussels on “How to coop the collaborative economy“, organized by major actors of the Belgian cooperative movement and building on the experience of a growing network of persons and organizations to enhance a cooperative view of the internet. Several themes in connection with my studies of the collaborative economy emerged, and I’d like to summarize here what were, in my view, the main lessons learned of the day.

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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?

autonomyI 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 islogos 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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