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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.06936 |
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| _version_ | 1866913604074209280 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Creating a Cooperative AI Policymaking Platform through Open Source Collaboration 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 Computers and Society Artificial Intelligence Machine Learning 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. |
| title | Creating a Cooperative AI Policymaking Platform through Open Source Collaboration |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.06936 |