Workshop on Social and Ethical Challenges in Machine Learning
This invitation-only workshop brought 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." Topics included 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.
The workshop was organized by Sanjeev Arora (IAS/Princeton University), Didier Fassin (IAS), Jacob Foster (UCLA), and Marion Fourcade (UC Berkeley/IAS)
Matthew Salganik (Princeton University), Sofia Ohlede (École Polytechnique Fédérale de Lausann), David Robinson (Cornell University), Bernard Harcourt (Columbia University), Arvind Narayanan (Princeton University)
Public Plenary Session Speakers:
Pedro Domingos (University of Washington), Mary Gray (Microsoft)