论文标题
对社会影响的人工智能：数据到数据管道中的学习和计划
AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline
论文作者
论文摘要
随着人工智能和多种系统研究的成熟，我们有很大的机会将这些进步引导到解决复杂的社会问题。为了追求AI对社会影响的目标，我们作为AI研究人员必须超越计算方法的改进。重要的是要走出现场以展示社会影响。为此，我们专注于低资源社区中的公共安全和保障，野生动植物保护和公共卫生的问题，并目前在多种系统中的研究进展，以应对一个关键的跨切割挑战：如何有效地在这些问题域中有限的干预资源。我们介绍了来自世界各地部署的案例研究，以及从对AI感兴趣的社会影响感兴趣的研究人员中学到的经验教训。在推动这项研究议程时，我们认为AI确实可以在与社会不公正和改善社会作斗争中发挥重要作用。
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.
