International Conference on Data Economy
June 22 and 23, 2017
IMT - Télécom ParisTech - 46, rue Barrault, Paris 13
The digital economy is now "data-driven". Big data, algorithms and artificial intelligence are profoundly changing the nature of competition between firms as well as customer relationship management. On the one hand, new data strategies based on smart connected objects create opportunities to challenge existing value chains and force companies to rethink the organization of their industry. Data accumulation by large companies generate increasing returns to scale that increase demand through targeting and recommendations. On the other hand, big data can improve product and service experience but raise at the same time concerns for consumers. For instance, business models based on advertising are both creating new ways to monetize audience but raise privacy, autonomy and trust concerns.
This international conference will focus on two dimensions of the data economy from a pluridisciplinary perspective. First, the event is part the official program of the French Data Protection Authority (Cnil) on ethics and algorithms and will highlight several keynotes on the topic. The second dimension that the conference will explore is the free flow of data in the digital economy, focusing on economic, legal and political aspects.
Thursday Program (13:30-18:00)
Online crime and security economics
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Nicolas Christin
Carnegie Mellon University
Over The Top (OTT) Video: Policy and Privacy Issues
Marvin Sirbu
Carnegie Mellon University
Accountability for Fairness in Data-Driven Systems
Anupam Datta
Carnegie Mellon University, Sillicon Valley
Our vision is to enable data-driven systems to be accountable for fairness properties. We use the term “accountable” to refer to computational mechanisms that can be used to “account for” behaviors of systems to support detection of fairness violations, as well as explain how they came about. We then leverage this understanding to repair systems and carefully account for the absence of violations.
As an illustration of this paradigm, I will introduce a notion of proxy non-discrimination and methods for making machine learning models accountable for this property. A key building block for these notions is Quantitative Input Influence -- a form of explanation for decisions of machine learning models. This work is motivated by recent discoveries of unfairness or bias in widely used data-driven systems, including our own work that discovered gender bias in the targeting of job-related ads in the Google advertising ecosystem.
Friday Program (09:00 - 12:30)
Industry 4.0 and the IoT: A View from Europe
Yann Ménière
European Patent Office, Chief economist
The current Debate on Data Ownership and Access to Data: First lessons from in-depth Industry Interviews
Klaus Wiedemann
Max Planck Institute for Innovation and Competition
Franziska Greiner
Max Planck Institute for Innovation and Competition
Update on recent developments in EC data policy
Kristina Kjerstad
European Commission
DG Communications Networks, Content and Technology
Data Policy and Innovation Unit