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AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and public safety.
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Stackelberg security games: Looking beyond a decade of success
Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, and Milind Tambe · 2018
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Prioritizing hepatitis c treatment in us prisons
Turgay Ayer, Can Zhang, Anthony Bonifonte, Anne C Spaulding, and Jagpreet Chhatwal · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Ai empowers conservation biology
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Ending discrimination in healthcare
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Artificial intelligence for social impact: Learning and planning in the data-to-deployment pipeline
Andrew Perrault, Fei Fang, Arunesh Sinha, and Milind Tambe · 2020
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Artificial intelligence and resource allocation in healthcare: The process-outcome divide in perspectives on moral decision-making
Sonia Jawaid Shaikh · 2020
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Artificial intelligence for social good: A survey
Zheyuan Ryan Shi, Claire Wang, and Fei Fang · 2020
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Ai for social good: unlocking the opportunity for positive impact
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Roma: Multi-agent reinforcement learning with emergent roles
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Rode: Learning roles to decompose multi-agent tasks
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M3rec: An offline meta-level model-based reinforcement learning approach for cold-start recommendation
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Guardhealth: Blockchain empowered secure data management and graph convolutional network enabled anomaly detection in smart healthcare
Ziyu Wang, Nanqing Luo, and Pan Zhou · 2020
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On the opportunities and risks of foundation models
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Envisioning communities: a participatory approach towards ai for social good
Elizabeth Bondi, Lily Xu, Diana Acosta-Navas, and Jackson A Killian · 2021
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A definition, benchmark and database of ai for social good initiatives
Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo, and Luciano Floridi · 2021
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Food web conservation vs. strategic threats: A security game approach
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Low-rank modular reinforcement learning via muscle synergy
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Clinical decision system using machine learning and deep learning: a survey
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Scalable decision-focused learning in restless multi-armed bandits with application to maternal and child health
Kai Wang, Shresth Verma, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, and Milind Tambe · 2023
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Reinforcement learning for dynamic dimensioning of cloud caches: A restless bandit approach
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Multi-agent systems and foundation models enable autonomous supply chains: Opportunities and challenges
Liming Xu, Sara Almahri, Stephen Mak, and Alexandra Brintrup · 2023
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A decision-language model (dlm) for dynamic restless multi-armed bandit tasks in public health
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