NeurIPS 2020
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Machine Learning for Economic Policy

Stephan Zheng · Alexander Trott · Annie Liang · Jamie Morgenstern · David Parkes · Nika Haghtalab

The goal of this workshop is to inspire and engage a broad interdisciplinary audience, including computer scientists, economists, and social scientists, around topics at the exciting intersection of economics, public policy, and machine learning. We feel that machine learning offers enormous potential to transform our understanding of economics, economic decision making, and public policy, and yet its adoption by economists and social scientists remains nascent.

We want to use the workshop to 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 significant positive socio-economic impact. In effect, we aim to accelerate the use of machine learning to rapidly develop, test, and deploy fair and equitable economic policies that are grounded in representative data.

For example, we would like to explore questions around whether machine learning can be used to help with the development of effective economic policy, to understand economic behavior through granular, economic data sets, to automate economic transactions for individuals, and how we can build rich and faithful simulations of economic systems with strategic agents. We would like to develop economic policies and mechanisms that target socio-economic issues including diversity and fair representation in economic outcomes, economic equality, and improving economic opportunity. In particular, we want to highlight both the opportunities as well as the barriers to adoption of ML in economics.

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Timezone: America/Los_Angeles