人工智能的社会影响:通过数据到部署的渠道进行学习和规划（Computers and Society）
- 2020 年 1 月 6 日
- 筆記
随着人工智能和多智能体系统研究的成熟，我们有巨大的机会将这些进展导向解决复杂的社会问题。为了探求人工智能的社会影响这一目标，我们作为人工智能研究人员必须超越改进计算方法;重要的是要走出去，在该领域展示社会影响。为此，我们关注公共安全与保障、野生动物保护和低资源社区的公共卫生问题，并介绍多代理系统的研究进展，以解决一个关键的交叉挑战：如何有效地在这些问题领域部署我们有限的干预资源。我们展示了我们在世界各地部署的案例研究，以及对那些对人工智能的社会影响感兴趣的研究人员有用的经验教训。在推动这项研究议程的过程中，我们相信人工智能确实可以在对抗社会不公和改善社会方面发挥重要作用。
原文题目：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
