EconML: Economics for Machine Learning

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EconML brings together researchers from machine learning, economics, game theory, and related fields to map the economic consequences of ML's growing success, anticipate emerging risks, and develop economic interventions that support long-term sustainability and benefit. The workshop bridges micro-scale mechanism design and market-level analysis, and we welcome contributions that connect economic ideas to core questions in theoretical and applied machine learning.

The workshop is organized around two complementary ways that economics and machine learning can inform one another:

• Economic Tools for Machine Learning: How can economic ideas improve learning and alignment, reveal the limitations of existing algorithms and paradigms, and align incentives in local interactions around AI systems?
• Machine Learning Ecosystems with Interacting Models: What economic phenomena emerge when many models interact in shared environments, and how can interventions improve ecosystem-level outcomes?

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