October 19-22, 2017
Brooklyn, NY

Janus Kopfstein

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Janus Cassandra Kopfstein is a New York-based researcher and journalist whose work focuses on information asymmetries, surveillance, and artificial intelligence. Her work has been featured in online and print publications including Vice/Motherboard, Al Jazeera, The New Yorker, and The Village Voice.


Manipulative Networks: Machine Obscurity and Human Legibility In The Age of A.I.

“The goal of everything we do is to change peoples’ actual behavior at scale.”

_~ Anonymous Silicon Valley Data Scientist_

In 2015, Harvard professor Shoshana Zuboff coined the term “surveillance capitalism” to describe a contemporary economic phenomenon involving the automated mass-extraction, analysis, and utilization of personal data. Illegibly implemented by tech behemoths like Facebook and Google, these mechanisms, she wrote, “effectively exile persons from their own behavior while producing new markets of behavioral prediction and modification.”

With the rise of neural networks, machine learning, and automation, these forces now thrive in an era where human activity and behavior are more legible to machines than ever before. The endless streams of data coursing through the veins of government datacenters and corporate information networks are no longer prized as dormant repositories of selfies, chats, and cat memes. Instead, they’ve become the raw materials for systems that analyze facial expressions, physical movements, speech/typing patterns, and more in order to predict and manipulate human behavior on a massive scale.

In this talk, I will draw on years of research and reporting on surveillance and artificial intelligence to survey the current landscape of automated machine manipulation – from Facebook’s infamous “emotional contagion” experiment to the firms responsible for spamming voters with hyper-personalized ads during the 2016 election. We will map out the various capture points and pathways taken by the training data extracted from everyday human activity, as well as highlight various methods for avoiding or subverting the manipulative processes that benefit.