Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2020
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.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
AI Magazine
Volume
41
Issue
4
First Page
3
Last Page
16
ISSN
0738-4602
Identifier
10.1609/aimag.v41i4.5296
Publisher
AI Access Foundation
Embargo Period
5-7-2021
Citation
Perrault, Andrew; FANG, Fei; SINHA, Arunesh; and TAMBE, Milind.
Artificial intelligence for social impact: Learning and planning in the data-to-deployment pipeline. (2020). AI Magazine. 41, (4), 3-16.
Available at: https://ink.library.smu.edu.sg/sis_research/5915
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aimag.v41i4.5296