Cornell Tech Digital Life Seminar Series

Thursdays 12:30 to 2pm
Cornell Tech Campus, Roosevelt Island, Tata Innovation Center, Room 131


Thursday 30 AUGUST | 2018
Jake Goldenfein | Digital Life Initiative, Cornell Tech
The Profiling Potential of Computer Vision


Over the past decade, researchers have been inves­ti­gat­ing new tech­nolo­gies for cat­e­goris­ing people based on phys­i­cal attrib­utes alone. Unlike pro­fil­ing with behav­ioural data cre­ated by inter­act­ing with infor­ma­tional envi­ron­ments, these tech­nolo­gies record and mea­sure data from the ‘real world’ and use it to make a deci­sion about the​ ‘world state’ – in this case a judge­ment about a person. Auto­mated Per­son­al­ity Analy­sis and Auto­mated Per­son­al­ity Recog­ni­tion, for instance, are grow­ing sub-​dis­ci­plines of com­puter vision and computer listening. This family of tech­niques has been used to gen­er­ate per­son­al­ity pro­files, assess­ments of sex­u­al­ity, polit­i­cal orientation and crim­i­nal­ propensity using facial mor­pholo­gies and speech expres­sions alone. These pro­fil­ing sys­tems do not target the con­tent of images or speech, but measure and analyse para-visual and para-sonic information to train classifiers for revealing non-visual information like personal typologies and behavioural predictions.

While the knowl­edge claims of these pro­fil­ing tech­niques are often ten­ta­tive, they increas­ingly deploy a vari­ant of ​‘big data epis­te­mol­ogy’ suggesting there is more infor­ma­tion in a human face or in spoken sound than is acces­si­ble or com­pre­hen­si­ble to humans. This paper explores the bases of those claims and the sys­tems of mea­sure­ment that are deployed in com­puter vision and lis­ten­ing. It asks if there is some­thing new in this class of data science knowledge claim, and attempts to under­stand what it means to com­bine com­pu­ta­tional empiri­cism, sta­tis­ti­cal analy­ses, and prob­a­bilis­tic rep­re­sen­ta­tions to pro­duce knowl­edge about people. Finally, the paper explores possible mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and people within it.

Thursday 06 SEPTEMBER | 2018
Glen Weyl | Microsoft Research
Data as Labor


Glen Weyl is a Principal Researcher at Microsoft Research New York City and teaches economics at Princeton University. His work on political economy seeks to combine economics, law, technology, philosophy to design radically egalitarian and inclusive markets that can address large scale social problems. He has published his research in leading journals in economics, law and computer science and has taught at the University of Chicago and Yale. However, he has recently turned towards communicating with and building a movement among a broader public. This began with is book Radical Markets: Uprooting Capitalism and Democracy for a Just Society joint with Eric Posner, but has continued in his work advising a wide range of start-ups developing Radical Markets ideas (especially in the blockchain space), helping organize a data labor movement, working with governments and political leaders around the world and collaborating with artists and other communicators to realize the true democratic potential of Radical Markets ideas.Glen is working to organize these strands into a coherent social movement through a variety of community-building activities and in particular is organizing a conference around Radical Markets, RadicalxChange, in March 2019.


Weyl will discuss “Data as Labor” as a conceptual frame, a set of organizational principles and a social movement. He will argue that conceiving of data as labor can make significant progress in resolving a number of theoretical and social problems associated with the exploitation of data and its creators, including the privacy-ownership dialectic, the paltry share of value added paid to labor in the high tech sector and the problematics of platform size and power. Weyl will discuss how data as labor suggests the need for a new kind of organization (“mediators of individual data” or MIDs) analogous to labor unions that would act as fiduciaries and loci of collective bargaining to protect data creators and describe eight principles for a successful MID. He will describe recent progress in creating a data labor movement and briefly conclude by placing it in a broader context of the Radical Markets agenda he has been developing.

Thursday 27 SEPTEMBER | 2018
Francesca Rossi | IBM AI Ethics Global Leader

Respondent: Daniel P. Huttenlocher | Cornell Tech, Dean and Vice Provost


Francesca Rossi is the IBM AI Ethics Global Leader, a distinguished research scientist at the IBM T.J. Watson Research Centre, and a professor of computer science at the University of Padova, Italy. Francesca’s research interest focuses on artificial intelligence, specifically constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues surrounding the development and behavior of AI systems, in particular for decision support systems for group decision making. A prolific author, Francesca has published over 190 scientific articles in both journals and conference proceedings as well as co-authoring A Short Introduction to Preferences: Between AI and Social Choice. She has edited 17 volumes, including conference proceedings, collections of contributions, special issues of journals, and The Handbook of Constraint Programming.

Leave a Reply

Your email address will not be published. Required fields are marked *