Fixing our Opaque, Fragmented and Disparate Big Data

par

Augustin Chaintreau

 Columbia University

The conference will be held in English.

ATTENTION: horaire et salle inhabituels pour un colloque du DIRO. 

Résumé 

Today's big data is flawed, and the threats it poses are not theoretical: We show with reproducible experiments that personalization algorithms in services used by millions pose moral hazards, that metrics of social endorsement are vastly misleading, and that the network dynamics facilitated by online interactions and sharing economies stand in the way of reducing various inequalities. Personal information collection and usage, however, ultimately bring benefits that we cannot forego, including in areas such as health, energy efficiency and public policies.

Opacity, Fragmented Views, and Disparate Impact may appear embedded in the fabric of Big Data; we show, on the contrary, from multiple examples that these trends can be reversed. Our first challenge is transparency and accountability in today's personalization, for which we provide a scalable solution validated on three leading services. We then address how to leverage opportunities offered by personal data in different domains, while informing mobile consumers and social media participants on their risks. Finally, we model how parsimonious individuals disparately benefit from information shared locally on a social network. Our analysis reveals general conditions on a network spectral expansion to eventually benefit all its members, closely related to the presence to special segregations between groups of users.

Biographie

Augustin is an Assistant Professor of Computer Science at Columbia University since 2010, where he directs the Mobile Social Lab. The goal of his research is to reconcile the benefits of leveraging personal data and social networks with a commitment to fairness and privacy. His latest results address transparency in personalization, the role of human mobility in privacy across several domains, the efficiency of crowdsourced content curation, the fairness of incentives to share personal data. His research lead to 25 papers in tier-1 conferences (five receiving best or best student paper awards at ACM CoNEXT, SIGMETRICS, USENIX IMC, IEEE MASS, Algotel), covered by several media including the NYT blog, The Washington Post, the Economist, or The Guardian. An ex student of the Ecole Normale Supérieure in Paris, he earned a Ph.D in mathematics and computer science in 2006, a NSF CAREER Award in 2013 and the ACM SIGMETRICS Rising star award in 2013. He has been an active member of network and web research community, organizing the upcoming Data Transparency Lab Conference, serving in the program committees of ACM SIGMETRICS (as chair), SIGCOMM, WWW, CoNEXT (as chair), MobiCom, MobiHoc, IMC, WSDM, WWW, COSN, AAAI ICWSM, and IEEE Infocom, and as area editor for IEEE TMC, ACM SIGCOMM CCR, and ACM SIGMOBILE MC2R.