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Consequential Decisions in Dynamic Environments
Niki Kilbertus · Angela Zhou · Ashia Wilson · John Miller · Lily Hu · Lydia T. Liu · Nathan Kallus · Shira Mitchell

Sat Dec 12 08:00 AM -- 03:50 PM (PST) @
Event URL: https://dynamicdecisions.github.io/ »

Machine learning is rapidly becoming an integral component of sociotechnical systems. Predictions are increasingly 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 welfaremits 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. Work in this area sits at the nexus of several different fields, and the workshop will provide an opportunity to better understand and synthesize social and technical perspectives on these issues and catalyze conversations between researchers and practitioners working across these diverse areas.

Author Information

Niki Kilbertus (Helmholtz AI)
Angela Zhou (Cornell University)
Ashia Wilson (UC Berkeley)
John Miller (University of California, Berkeley)
Lily Hu (Harvard University)

Lily Hu is a PhD candidate in Applied Mathematics and Philosophy at Harvard University. She works on topics in machine learning, algorithmic fairness, and (political) philosophy of technology. Her current time is divided between computer science-related research, where she studies theoretical properties and behaviors of machine learning systems as they bear on deployment in social and economic settings, and philosophical work, where she thinks about causal reasoning about categories like race, theories of discrimination, and what about current technological trends makes capitalism even more distressing.

Lydia T. Liu (University of California, Berk)
Nathan Kallus (Cornell University)
Shira Mitchell (Civis Analytics)

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