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Creating a Cooperative AI Policymaking Platform through Open Source Collaboration

Authors :
Lewington, Aiden
Vittalam, Alekhya
Singh, Anshumaan
Uppuluri, Anuja
Ashok, Arjun
Athmaram, Ashrith Mandayam
Milt, Austin
Smith, Benjamin
Weinberger, Charlie
Sarin, Chatanya
Bergmeir, Christoph
Chang, Cliff
Patel, Daivik
Li, Daniel
Bell, David
Cao, Defu
Shin, Donghwa
Kang, Edward
Zhang, Edwin
Li, Enhui
Chen, Felix
Smithline, Gabe
Chen, Haipeng
Gasztowtt, Henry
Shin, Hoon
Zhang, Jiayun
Gray, Joshua
Low, Khai Hern
Patel, Kishan
Cooke, Lauren Hannah
Burstein, Marco
Kalapatapu, Maya
Mittal, Mitali
Chen, Raymond
Zhao, Rosie
Majid, Sameen
Potlapalli, Samya
Wang, Shang
Patel, Shrenik
Li, Shuheng
Komaragiri, Siva
Lu, Song
Siangjaeo, Sorawit
Jung, Sunghoo
Zhang, Tianyu
Mao, Valery
Krishnakumar, Vikram
Zhu, Vincent
Kam, Wesley
Li, Xingzhe
Liu, Yumeng
Publication Year :
2024

Abstract

Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2412.06936
Document Type :
Working Paper