Computational Social Science Seminar: Algorithmic Fairness in Large Networks

March 25, 2019
1:00 - 2:30 PM


IAB 270 - ISERP Conference Room International Affairs Building 420 West 118th St New York, NY, 10027

Event Type: 



Algorithmic Fairness in Large Networks

Augustin Chaintreau, Associate Professor of Computer Science, Columbia University

Monday, March 25, 1-2:30PM

ISERP Conference Room (IAB 270)

Lunch provided | RSVP required

Seminar Abstract:

If you read technology news, or unfortunately perhaps in your own experience, you may have come across several examples of algorithmic unfairness. Disparate treatment or unequal outcomes where algorithmically induced decisions are perpetuating prior stereotypes or reinforcing inequalities. Where are those? Do we know where they may coming from? And what can computer scientists do about it?

The first half of this talk aims at surveying this nascent area. Its aim is to introduce three foundations of a fair deployment of Big Data (Privacy, Fair Algorithm, Transparency) to highlight their complementary merits. Vignettes of recent results will be introduced, as provocation for future discussions. It is also a good time to step back and distinguish what our field does to follow current successes and, in contrast, what may be more successful or needed in the future. The second half of the talk presents a case where seemingly benign random algorithm, of the kind used everyday for unsupervised learning, exacerbates the ``glass ceiling’’ present in social network that makes members of minority unlikely to reach a prominent status. We conclude that to expand and scale fairness methods to our intense and uncoordinated networked lives we need better models and algorithms that learn to correct themselves.

Speaker Bio: 

Augustin Chaintreau is an Associate 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 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 and algorithms used in social networking. His research lead to 35 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 the network and web research community, serving in the program committees of ACM SIGMETRICS (as chair), FAT*, SIGCOMM, WWW, CoNEXT (as chair), EC, MobiCom, MobiHoc, IMC, WSDM, COSN, AAAI ICWSM, and IEEE Infocom, as area editor for IEEE TMC, ACM SIGCOMM CCR, ACM SIGMOBILE MC2R, and editor in chief for PACM POMACS.



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