EpilogueGetting Good at AI for Good

—The AI for Good Lab

AI for Good (AI4G) projects focus on developing AI-based solutions to advance goals in areas such as sustainability, health, humanitarian aid, and social justice. Through over 100 AI4G projects in the last five years, we have gained insights from successful strategies and, crucially, we have learned from mistakes. The most significant lesson that we have learned in this process is the importance of collaborating with partner organizations that have expertise in the relevant domain and experience in working toward specific goals in sustainability, humanitarian action, and health. This epilogue focuses on learnings from these experiences, covering different aspects of collaborations with partners, including communication, data, modeling, and impact. We also address the unique challenges of AI4G projects compared to machine learning projects in academic and corporate settings and outline strategies for successfully undertaking these projects. Finally, we distill 11 key takeaways to guide AI4G projects in general.

Communication

In the realm of AI4G projects, effective communication between data scientists and partner organizations, which are typically domain experts, is crucial. Here, we provide three areas of insight that data scientists can use to enhance this relationship for the success of AI4G projects.

Setting Realistic Expectations for AI

Partner organizations often have inflated expectations about AI capabilities, ...

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