Event
Augustin Chaintreau, Columbia University
Wednesday, November 2, 2016 15:30to16:30
Room 6214 , Pavillon Andr茅-Aisenstadt, 2920, Chemin de la tour, 5th floor, Montreal, QC, H3T 1J4, CA
Fixing our opaque, fragmented and disparate big data.
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.
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.