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UID:20250718T212808EDT-6361zuu7G5@132.216.98.100
DTSTAMP:20250719T012808Z
DESCRIPTION:Fixing our opaque\, fragmented and disparate big data.\n\nToday
 's big data is flawed\, and the threats it poses are not theoretical: We s
 how with reproducible experiments that personalization algorithms in servi
 ces used by millions pose moral hazards\, that metrics of social endorseme
 nt are vastly misleading\, and that the network dynamics facilitated by on
 line interactions and sharing economies stand in the way of reducing vario
 us inequalities. Personal information collection and usage\, however\, ult
 imately bring benefits that we cannot forego\, including in areas such as 
 health\, energy efficiency and public policies.\n	\n	Opacity\, Fragmented Vi
 ews\, 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 tod
 ay's personalization\, for which we provide a scalable solution validated 
 on three leading services. We then address how to leverage opportunities o
 ffered by personal data in different domains\, while informing mobile cons
 umers and social media participants on their risks. Finally\, we model how
  parsimonious individuals disparately benefit from information shared loca
 lly on a social network. Our analysis reveals general conditions on a netw
 ork spectral expansion to eventually benefit all its members\, closely rel
 ated to the presence to special segregations between groups of users.\n
DTSTART:20161102T193000Z
DTEND:20161102T203000Z
LOCATION:Room 6214 \, CA\, QC\, Montreal\, H3T 1J4\, Pavillon André-Aisenst
 adt\, 2920\, Chemin de la tour\, 5th floor
SUMMARY:Augustin Chaintreau\, Columbia University
URL:/mathstat/channels/event/augustin-chaintreau-colum
 bia-university-263858
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