Machine Learning and the Social Sciences

Two Workshops Co-organized by the
School of Mathematics
and the
School of Social Science

Sanjeev Arora, Princeton University/Institute for Advanced Study
Didier Fassin, Institute for Advanced Study
Jacob Foster, University of California, Los Angeles
Marion Fourcade, University of California, Berkeley/Institute for Advanced Study

First Workshop: Workshop on Social and Ethical Challenges in Machine Learning
Date: November 6, 2019
Location: Institute for Advanced Study, Princeton

This invitation-only workshop will bring together experts in machine learning and social scientists in an effort to reflect on the social and ethical challenges of producing machine learning and using it "in the wild." Possible topics include the social and ethical issues related to: the global digital labor market that supports many practical applications of ML; the impact of AI advances on work and occupations; unchartered territories for the application of new learning methods; public attitudes toward (and understandings of) artificial intelligence; deep fakes, democracy and the transformation of civic discourses; possible biases and discrimination embedded in predictive analytics (e.g. from search to policing and sentencing, from HR to dating, from social services to marketing); human sense making, opacity and machine learning outcomes; the promises and pitfalls of using AI to manage and control individuals and populations (e.g. via the generalized surveillance and scoring of individuals, as in the social credit system); the AI-induced reconfiguration of emotions, desires, and cognition; and cross-national differences in the implementation and regulation of machine learning.

Workshop Schedule
Public Event

Second Workshop: Re-Imagining the Social Sciences in the Age of AI: A Cross-Disciplinary Conversation
Date: March 4-5, 2020
Location: Institute for Advanced Study, Princeton

Can the dramatic recent progress in machine learning lead to conceptual advances in the social sciences and humanities? Although we have access to social and cultural datasets of unprecedented scale, quality, and complexity, this question remains open. New methods of machine learning (e.g., various flavors of deep nets, transformer nets, AlphaGo and the like) often take a "black box" view of data. Little theoretical understanding exists about what patterns in data were implicitly identified as part of the learning. But it is these patterns that are of primary interest in the social sciences, especially when researchers hope to discover new social processes or phenomena. This workshop aims to bridge this gap via focused dialog between the two communities. ML experts will learn about questions in culture, cognition, social action, power relations etc. that might inform the design of ML systems in the laboratory and “in the wild.” Social scientists will arrive at a better understanding of how modern ML techniques might be leveraged to generate new research projects and transformative methodological innovations. Out-of-the-box talks and discussions are highly encouraged!

Workshop Schedule
Public Event