Artificial Intelligence for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

Authors

  • Andrew Perrault Harvard University
  • Fei Fang Carnegie Mellon University
  • Arunesh Sinha Singapore Management University
  • Milind Tambe Harvard University

DOI:

https://doi.org/10.1609/aimag.v41i4.5296

Abstract

With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward 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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Published

2020-12-28

How to Cite

Perrault, A., Fang, F., Sinha, A., & Tambe, M. (2020). Artificial Intelligence for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline. AI Magazine, 41(4), 3-16. https://doi.org/10.1609/aimag.v41i4.5296

Issue

Vol. 41 No. 4: Winter 2020

Section

Special Topic Articles

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