Algorithmic Fairness in Large Networks

Abstract: A. Darwiche suggested that being ``bullied-by-success'' is common across scientific fields and describes current dynamics in the AI and Machine Learning communities. Could it be, ironically, that Fair Machine Learning - a field that started by the need to address a 'blind spot' in AI - also starts to be bullied by its own success? Then would be a good time to step back and distinguish what follows from current successes and, in contrast, what may be more successful or needed in the future. This is what motivates this talk. I will briefly overview the three foundations of a fair deployment of Big Data (Privacy, Algorithmic Fairness, Transparency) to highlight their complementary merits. We then present recent examples where our research addressed what it would take to expand and scale those methods to our intense and uncoordinated networked lives: we will focus on fairness in unsupervised personalization but also mention parallels with inter-data-domain privacy and transparency in the face of intensifying targeting.

Bio:Augustin Chaintreau is an Assistant Professor of Computer Science at Columbia University where he directs the Mobile Social Lab. His research tries to balance the benefits of leveraging personal data and social networks with protecting 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 and the fairness of incentives to share personal data. He has had 25 papers accepted to tier-1 conferences (five receiving top paper awards —at ACM CoNEXT, SIGMETRICS, USENIX IMC, IEEE MASS and Algotel) and his reseearch has appeared in The New York Times, Washington Post, Economist, and Guardian. A graduate of the Ecole Normale Supérieure in Paris, he earned a Ph.D in mathematics and computer science in 2006, an NSF CAREER Award in 2013 and an ACM SIGMETRICS Rising star award in 2013. He has been active in the network and web research community, organizing the upcoming Data Transparency Lab Conference, serving on the program committees of ACM SIGMETRICS (chair), SIGCOMM, WWW, CoNEXT (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.