Position: Social Environment Design Should be Further Developed for AI-based Policy-Making

Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:60527-60540, 2024.

Abstract

Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI in automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policymaking. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-zhang24cl, title = {Position: Social Environment Design Should be Further Developed for {AI}-based Policy-Making}, author = {Zhang, Edwin and Zhao, Sadie and Wang, Tonghan and Hossain, Safwan and Gasztowtt, Henry and Zheng, Stephan and Parkes, David C. and Tambe, Milind and Chen, Yiling}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {60527--60540}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cl/zhang24cl.pdf}, url = {https://proceedings.mlr.press/v235/zhang24cl.html}, abstract = {Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI in automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policymaking. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.} }
Endnote
%0 Conference Paper %T Position: Social Environment Design Should be Further Developed for AI-based Policy-Making %A Edwin Zhang %A Sadie Zhao %A Tonghan Wang %A Safwan Hossain %A Henry Gasztowtt %A Stephan Zheng %A David C. Parkes %A Milind Tambe %A Yiling Chen %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-zhang24cl %I PMLR %P 60527--60540 %U https://proceedings.mlr.press/v235/zhang24cl.html %V 235 %X Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI in automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policymaking. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.
APA
Zhang, E., Zhao, S., Wang, T., Hossain, S., Gasztowtt, H., Zheng, S., Parkes, D.C., Tambe, M. & Chen, Y.. (2024). Position: Social Environment Design Should be Further Developed for AI-based Policy-Making. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:60527-60540 Available from https://proceedings.mlr.press/v235/zhang24cl.html.

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