Becoming Good at AI for Good

TL;DR

This paper discusses best practices and lessons learned from collaborative AI for Good projects across domains like sustainability and health, emphasizing communication, data, modeling, and impact.

Contribution

It provides a structured framework with eleven key takeaways for effective collaboration in AI for Good initiatives based on practical experience.

Findings

Identified four key collaboration aspects: communication, data, modeling, impact.

Presented two case studies demonstrating application of best practices.

Outlined eleven practical takeaways for future AI for Good projects.

Abstract

AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level categories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations.