Becoming Good at AI for Good
Becoming Good at AI for Good
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DOI：
10.1145/3461702.3462599
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Meghana Kshirsagar;Caleb Robinson;Siyu Yang;Shahrzad Gholami;I. Klyuzhin;S. Mukherjee;Md Nasir;Anthony Ortiz;Felipe Oviedo;Darren Tanner;Anusua Trivedi;Yixi Xu;Ming Zhong;B. Dilkina;R. Dodhia;J. Ferres
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作者：
Meghana Kshirsagar;Caleb Robinson;Siyu Yang;Shahrzad Gholami;I. Klyuzhin;S. Mukherjee;Md Nasir;Anthony Ortiz;Felipe Oviedo;Darren Tanner;Anusua Trivedi;Yixi Xu;Ming Zhong;B. Dilkina;R. Dodhia;J. Ferres
AI for good （AI4G）项目涉及开发和应用基于人工智能（AI）的解决方案，以实现可持续性、健康、人道主义援助和社会正义等领域的进一步目标。开发和部署此类解决方案必须与相关领域的专家以及已经在实现这些目标方面取得进展的经验的伙伴合作完成。根据我们的经验，我们详细地将这种类型的协作的不同方面分为四个高级类别：通信、数据、建模和影响，并提炼出11个要点来指导未来的此类项目。我们简要地描述了两个案例研究，以说明在我们过去的合作中如何将这些要点应用于实践。
AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level categories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations.