Section: New Results

Computational Social Sciences

Computational Social Sciences (CSS) is making significant progress in the study of social and economic phenomena thank to the combination of social science theories and new insight from data science. While the simultaneous advent of massive data and unprecedented computational power has opened exciting new avenues, it has also raised new questions and challenges.

Several studies are being conducted in TAU, about labor (labor markets, platform "micro-work", quality of life and economic performance), about nutrition (health, food, and socio-demographic issues), around Cartolabe, a platform for scientific information system and visual querying and around GAMA, a multi-agent based simulation platform.

Labor Studies

Participants: Philippe Caillou, Isabelle Guyon, Michèle Sebag, Paola Tubaro

PhDs: Diviyan Kalainathan, Guillaume Bied, Armand Lacombe

Post-Docs: Saumya Jetley

Engineers: Raphael Jaiswal, Victor Alfonso Naya

Collaboration: Jean-Pierre Nadal (EHESS); Marco Cuturi, Bruno Crépon (ENSAE); Antonio Casilli, Ulrich Laitenberger (Telecom Paris); Odile Chagny (IRES); Alessandro Delfanti (University of Toronto)

A first area of activity of TAU in Computational Social Sciences is the study of labor, from the functioning of the job market, to the rise of new, atypical forms of work in the networked society of internet platforms, and the quality of life at work.

Job markets Two projects deal with the domain of job markets and machine learning. The DataIA project Vadore, in collaboration with ENSAE and Pôle Emploi, has two goals. First, to improve the recommendation of jobs for applicants (and the recommendation of applicants to job offers). The main originalities in this project are: i) to use both machine learning and optimal transport to improve the recommendation by learning a matching function for past hiring, and then to apply optimal transport-like bias to tackle market congestion (e.g. to avoid assigning many applicants to a same job offer); ii) to use randomized test on micro-markets (AB testing) in collaboration with Pôle Emploi to test the global impact of the algorithms.

The JobAgile project, BPI-PIA contract, coll. EHESS, Dataiku and Qapa, deals with low salary interim job recommendations. A main difference with the Vadore project relies on the high reactivity of the Qapa and Dataiku startups: i) to actually implement AB-testing; ii) to explore related functionalities, typically the recommendation of formations; iii) to propose a visual querying of the job market, using the Cartolabe framework (below).

The platform economy and digital labor

Another topic concerns the digital economy and the transformations of labor that accompany it. One part of the platform economy carries promises of social, not only techno-economic, innovation. If enthusiasms for a new "sharing economy" or "collaborative economy" have progressively faded away, values of decentralization, autonomy, and flatter coordination are still commonly associated to platforms. A conference paper by P. Tubaro studies how events constitute places where actors of the platform economy negotiate values and collectively drive forms of social change [43].

The platform economy and its effects on labor are also linked to the current developments of AI [22]. In collaboration with A.A. Casilli (Telecom ParisTech), P. Tubaro has received funding from the Union Force Ouvrière, from France Stratégie (a Prime Minister's service), and from MSH Paris-Saclay, to map "micro-work" in France (DiPLab project). The term micro-work refers to small, data-related tasks that are performed online against low remunerations, such as tagging objects in images, transcribing bits of text, and recording utterances aloud. Specialized platforms such as Amazon Mechanical Turk, Clickworker and Microworkers recruit online providers to execute these tasks for their clients, mostly for data-intensive production processes. In addition to poor working conditions and low pay, micro-work raises issues in terms of privacy and data protection, insofar as outside providers are entrusted with data that may include personal information [23].

The results of the DiPLab study were published in a report that attracted significant media attention [52], and presented as part of a large event on micro-work at the headquarters of France Stratégie in June 2019. Two articles relying on DiPlab results, one of which was first published as a working paper [62], [61], are now under review.

A joint franco-Canadian grant obtained by P. Tubaro and A. Delfanti (University of Toronto) enabled the creation of an "International Network on Digital Labor", aiming to bring together scholars interested in various forms of digital platform labor. Inauguration of the network involved the organization of two workshops, one in Paris (June 2019) and the other in Toronto (October 2019).

Further research on how digital platforms transform labor practices and affect the very definition of professions is being undertaken by P. Tubaro as part of a two-year (2018-2020) grant from DARES (French Ministry of Labor), in collaboration with O. Chagny of IRES, a union-funded think-tank), and A.A. Casilli (Telecom ParisTech).

A newly-obtained grant from ANR (with A.A. Casilli and U. Laitenberger, Telecom ParisTech) will enable P. Tubaro to further explore the global production networks that link AI developers and producers to data-related work across national boundaries, following outsourcing chains that extend from France to French-speaking African countries, and from Spain and the USA to parts of Latin America. This new project, entitled "The HUman Supply cHain behind smart technologies" (HUSH), will start in January 2020.

Health, food, and socio-demographic relationships

Participants: Philippe Caillou, Michèle Sebag, Paola Tubaro

Post-doc: Ksenia Gasnikova

Collaboration: Louis-Georges Soler, Olivier Allais (INRA)

Another area of activity concerns the relationships between eating practices, socio-demographic features and health.

The Nutriperso project (IRS Univ. Paris-Saclay, coll. INRA, CEA, CNRS, INSERM, Telecom ParisTech and Univ. Paris-Sud) aims to: i) determine the impact of food items on health (e.g., related to T2 diabetes); ii) identify alternative food items, admissible in terms of taste and budget, and better in terms of health; iii) emit personalized food recommendations> One project motivation is the fact that general recommendations (e.g., Eat 5 fruit and vegetable per day) are hardly effective on populations at risk. Based on the Kantar database, reporting the food habits of 20,000 households over 20 years, our challenge is to analyze the food purchases at an unprecedented fine-grained scale (at the barcode level), and to investigate the relationship between diets, socio-demographic features, and body mass index (BMI). The challenge also regards the direction of causality; while some diets are strongly correlated to high BMI, the question is to determine whether, e.g., sugar-free sodas are a cause of obesity, or a consequence thereof, or both a cause and a consequence. A main difficulty is the lack of control populations to assess a diet impact. Such a control population could be approximated in the case of the organic diet, showing a statistically significant impact of this diet on the BMI distribution. The question of finding confounders (e.g. based on wealth or education) or "backdoor" variables in under study.

Scientific Information System and Visual Querying

Participants: Philippe Caillou, Michèle Sebag

Engineers: Anne-Catherine Letournel, Jonas Renault

Collaboration: Jean-Daniel Fekete (AVIZ, Inria Saclay)

A third area of activity concerns the 2D visualisation and querying of a corpus of documents. Its initial motivation was related to scientific organisms, institutes or Universities, using their scientific production (set of articles, authors, title, abstract) as corpus. The Cartolabe project started as an Inria ADT (coll. Tao and AVIZ, 2015-2017). It received a grant from CNRS (coll. Tau , AVIZ and HCC-LRI, 2018-2019). Further extensions, as an open-source platform, are under submission at the time of writing.

The originality of the approach is to rely on the content of the documents (as opposed to, e.g. the graph of co-authoring and citations). This specificity allowed to extend Cartolabe to various corpora, such as Wikipedia, Bibliotheque Nationale de France, or the Software Heritage. Cartolabe was also applied in 2019 to the Grand Debat dataset: to support the interactive exploration of the 3 million propositions; and to check the consistency of the official results of the Grand Debat with the data.

Among its intended functionalities are: the visual assessment of a domain and its structuration (who is expert in a scientific domain, how related are the domains); the coverage of an institute expertise relatively to the general expertise; the evolution of domains along time (identification of rising topics). A round of interviews with beta-user scientists is under way since end 2019. Cartolabe usage raises questions at the crossroad of human-centered computing, data visualization and machine learning: i) how to deal with stressed items (the 2D projection of the item similarities poorly reflects their similarities in the high dimensional document space; ii) how to customize the similarity and exploit the users' feedback about relevant neighborhoods.

Multi-Agent based simulation framework for social science

Participants: Philippe Caillou

Collaboration: Patrick Taillandier (INRA), Alexis Drogoul and Nicolas Marilleau (IRD), Arnaud Grignard (MediaLab, MIT), Benoit Gaudou (Université Toulouse 1)

Since 2008, P. Caillou contributes to the development of the GAMA platform, a multi-agent based simulation framework. Its evolution is driven by the research projects using it, which makes it very well suited for social sciences studies and simulations.

The focus of the development team in 2019 was on the stability of the platform and on the documentation to provide a stable and well documented framework to the users.