Machine learning offers enormous potential to transform our understanding of economics, economic decision making, and public policy. Yet its adoption by economists, social scientists, and policymakers remains nascent.
This workshop will highlight both the opportunities as well as the barriers to the adoption of ML in economics. In particular, we aim to accelerate the use of machine learning to rapidly develop, test, and deploy effective economic policies that are grounded in representative data.
This workshop will expose some of the critical socio-economic issues that stand to benefit from applying machine learning, expose underexplored economic datasets and simulations, and identify machine learning research directions that would have a significant positive socio-economic impact. This includes policies and mechanisms that target socio-economic issues such as diversity and fair representation in economic outcomes, economic equality, and improving economic opportunity.
David C. Parkes
We will award two best paper prizes from the contributed works. The two awards will recognize methodological and empirical contributions, respectively. Each best paper will be invited to give an oral presentation during the workshop.
We will provide financial support (registration award) for authors with accepted papers, speakers, and attendees on a needs-based basis, sponsored by Salesforce. Based on demand, there will be a limit to the number of attendees we will support. Please fill out and apply via this form: https://forms.gle/WMhnyqz6vZe74b2Z7.
This workshop is non-archival. All accepted papers will be presented as virtual posters and invited to record 5-minute videos, with exceptional submissions also presented as 20-minute oral presentations.
Submissions should not include work that was published or first made available before January 1, 2017. Work that has been published or has appeared on January 1, 2017, or later is acceptable. Work that is in submission / under review is acceptable.
Questions? Email us: email@example.com.