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.