Machine learning is becoming an integral component of sociotechnical systems. Predictions are used to grant beneficial resources or withhold opportunities, and the consequences of such decisions induce complex social dynamics by changing agent outcomes and prompting individuals to proactively respond to decision rules. This introduces challenges for standard machine learning methodology. Static measurements and training sets poorly capture the complexity of dynamic interactions between algorithms and humans. Strategic adaptation to decision rules can render statistical regularities obsolete. Correlations momentarily observed in data may not be robust enough to support interventions for long-term welfare.
Recognizing the limits of traditional, static approaches to decision-making, researchers in fields ranging from public policy to computer science to economics have recently begun to view consequential decision-making through a dynamic lens. This workshop will confront the use of machine learning to make consequential decisions in dynamic environments.
- Rediet Abebe (Harvard Society of Fellows, UC Berkeley)
- Hamsa Bastani (University of Pennsylvania)
- Yiling Chen (Harvard)
- Moritz Hardt (UC Berkeley)
- Mitsue Iwata (Planned Parenthood Federation of America)
- Maximilian Kasy (University of Oxford)
- Daniel Kuhn (EPFL)
Call for Submissions & Important Dates
Please see the Call for papers for submission instructions.
- Submission deadline: October 9, 2020, Anywhere on Earth
- Notification of acceptance: October 30, 2020
- Workshop date: Saturday, December 12, 2020