Data and Algorithmic Transparency and Accountability
Inria Associate Team 2018-2020
Privatics, Inria, France - LATECE, UQAM, Canada

Data and Algorithmic Transparency and Accountability

The recent advent of personalized services, which are tailored according to the interests of their users, has lead to the massive collection of personal data and the construction of detailed profiles about these users. However, in the current Internet paradigm, there is a strong asymmetry in terms of power and information between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, ...) and the individuals to whom those data are related. As a consequence, users of information technologies have no choice but to trust the entities collecting and using their data. This lack of transparency gives rise to ethical issues such as loss of control over personal data, discrimination, and unfair processing. For instance, controversial practices have been observed such as price discrimination (i.e., customizing prices for users according to their browsing history or their location), gender discrimination as well as price steering (i.e., changing the order of search results to highlight specific products). Unfair treatments can result from biases in the data or wrong predictions made by the machine learning process used to perform the personalization. In addition, the structure of these machine learning algorithms is often complex and producing intelligible explanations about their results is quite challenging. All these issues become even more critical when this type of system is used to help decision making (or to make decisions) in sectors such as justice or health.

To address these issues, data and algorithmic transparency is a growing and emerging research area. In addition, increasing the transparency of algorithms and opening them to the scrutiny of the public or to independent authorities such as the CNIL is only the first step in order to make them more accountable. In particular, once the opacity of personalization algorithms has been lifted, one of the long-term objective is to able to measure and reduce the discrimination in these algorithms. We believe that the data and algorithmic transparency and accountability should address three aspects: (1) the traceability of the collection of personal data; (2) the verification of the compliance of algorithms with critical properties (and legal requirements) such as non-discrimination and fairness, and (3) the capacity to explain the results of algorithmic systems. The proposed Associate Team will build on the expertise of the partners to address these three complementary aspects.

The main scientific objective of this collaboration is to be able to advance the research on data and algorithmic transparency and accountability by studying it in concrete but diverse contexts. The first context that we propose to investigate thoroughly is how personal information related to the physical world, such as the presence and mobility data of users, are collected and exploited by companies to perform behavioral targeting.

The challenges are multiple. First, cyber-physical tracking systems are often passively collecting data or use deceptive techniques, and are thus hard to detect. Second, the collection and analysis of data is also a challenging task as we need to capture enough data to be able to infer and model the link between the activities of the users (in particular their mobility) and the personalized content. Finally, even if enough data are available, inferring the behavior of an algorithm is not trivial as we may only be able to interact with it as a black-box and thus we may need to approximate its behavior following a machine learning approach.

To overcome these challenges, we will split the research program of the associated team into three phases. During the first phase, we have identified existing tracking systems and studies and reconstructed their underlying mechanisms. Then, during a second phase, we will design a methodology to quantify how well a particular system meets requirements such as non-discrimination and fairness. And finally, during the third phase, we will develop methods for improving the intelligibility of systems, for example through the generation of global explanations of the logic of the system and local explanations about specific results. We will put emphasis on the interactions with the users to enhance their understanding of the system.

We will then build on these results to develop broader mechanisms that could be applied to other areas such as predictive justice and health. The investigation of this area will be done in collaboration with researchers in law from Québec and with experts of algorthmic decision making in the medical sector (such as the designers and operators of algorithms used in France to take decisions about organ transplantations).


Théo Jourdan

PhD Student, Privatics research group, Inria, France

Clément Henin

PhD Student, Privatics research group, Inria, France

Marie-Jean Meurs

Assistant Professor, LATECE research group, UQAM, Québec

Guillaume Célosia

PhD Student, INSA-Lyon, Inria, Privatics research group, Lyon, France

Sébastien Gambs

Associate Professor, LATECE research group, UQAM, Québec

Mathieu Cunche

Assistant Professor, INSA-Lyon, Inria, Privatics research group, Lyon, France

Daniel Le Métayer

Senior Researcher, Inria, Privatics research group, Lyon, France

Rosin Claude Ngueveu

PhD Student, LATECE research group, UQAM, Québec

Antoine Boutet

Assistant Professor, INSA-Lyon, Inria, Privatics research group, Lyon, France


  • Events

    • January 24th, 2018:
      Panel on "Physical tracking: nowhere to hide?"
      Panel organised by Daniel Le Métayer involving Mathieu Cunche at CPDP (11th international Conference on Computers Privacy and Data Protection), Brussels, Belgium.

    • March 5-8, 2018:
      Shonan Meeting on Anonymization methods and inference attacks: theory and practice
      This event included multiple experts in the field, Shonan Village, Japan.
      More information about this event

    • April 23th, 2018:
      Workshop on Data Transparency
      This event organised at Lyon (France) included 9 speakers and more than 40 attendees.
      More information about this workshop

    • May 18th, 2018:
      Journée thématique "Intelligibilité et Transparence du Machine Learning et des Intelligences Artificielles"
      Daniel Le Métayer : Transparence, intelligibilité des algorithmes : quels besoins ? Quels moyens ?
      Conférence organisée par la SFdS (Société Française de Statistique), Paris, France.

    • November 15th, 2018:
      Panel on "Cybersécurité : se préparer au Tsunami"
      Sommet des Start-up Challenge, Lyon.
      More information about this event

    • November 21th, 2018:
      Colloque HumanIA 2018, Montréal, Quebec
      This event included multiple speakers and tutorials.
      More information about this event

    • October - December, 2018:
      Challenge on data anonymisation
      Challenge organised between three different groups of students at Insa Lyon and Bourges.

  • Publications

    • Antoine Boutet, Alexandre van Beurden , Sébastien Gambs
      Inspect what your location history reveals about you
      Demonstration given at OPERANDI 2018 (Open Day for Privacy, Transparency and Decentralization) collocated to PETS 2018, Barcelona, Spain, 2018.

    • Guillaume Celosia, Mathieu Cunche
      Detecting smartphone state changes through a Bluetooth based timing attack
      Paper published at WiSec 2018, Stockholm, Sweden, 2018.

    • Mohammad Alaggan, Mathieu Cunche, and Sébastien Gambs
      Privacy-preserving Wi-Fi Analytics
      Paper published at PETS 2018, Barcelona, Spain, 2018.

    • Théo Jourdan, Antoine Boutet, Carole Frindel
      Toward privacy in IoT mobile devices for activity recognition
      Paper published at Mobiquitous 2018, New York, United States.

    • Claude Castelluccia, Daniel Le Métayer
      Understanding algorithmic : opportunities and challenges
      Paper published by the European Parliament, Scientific Foresight Unit (STOA), 2018.

    • Sourya Joyee De, Daniel Le Métayer
      Privacy risk analysis to enable informed privacy settings
      Paper published at International Workshop on Privacy Engineering (IWPE), Euro S&P Workshops, 2018.

    • Sébastien Gambs, Julien Lolive, Jean-Marc Robert
      Entwining Sanitization and Personalization on Databases
      Paper published at Asia Conference on Computer and Communications Security (AsiaCCS), 2018.

    • Daniel Le Métayer, Pablo Rauzy
      Capacity: an abstract model of control over personal data
      Paper published at Conference on Data and Application Security and Privacy (CODASPY), 2018.

    • Marc Queudot, Marie-Jean Meurs
      Artificial Intelligence and Predictive Justice: Limitations and Perspectives
      Paper published at International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2018.

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