人工智慧的社會影響:通過數據到部署的渠道進行學習和規劃（Computers and Society）

• 2020 年 1 月 6 日
• 筆記

隨著人工智慧和多智慧體系統研究的成熟，我們有巨大的機會將這些進展導向解決複雜的社會問題。為了探求人工智慧的社會影響這一目標，我們作為人工智慧研究人員必須超越改進計算方法;重要的是要走出去，在該領域展示社會影響。為此，我們關注公共安全與保障、野生動物保護和低資源社區的公共衛生問題，並介紹多代理系統的研究進展，以解決一個關鍵的交叉挑戰：如何有效地在這些問題領域部署我們有限的干預資源。我們展示了我們在世界各地部署的案例研究，以及對那些對人工智慧的社會影響感興趣的研究人員有用的經驗教訓。在推動這項研究議程的過程中，我們相信人工智慧確實可以在對抗社會不公和改善社會方面發揮重要作用。

原文題目：AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

原文：With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.

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原文作者：Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

原文地址：https://arxiv.org/abs/2001.00088