# Becoming Good at AI for Good

arXiv: https://arxiv.org/abs/2104.11757 · HTML: https://mlsanity.com/paper/2104.11757/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2021.

## Built on by (most cited first, 8 of 8)

- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [Galileo: Learning Global & Local Features of Many Remote Sensing Modalities](https://mlsanity.com/paper/2502.09356/) (2025)
- [Geospatial Foundation Models to Enable Progress on Sustainable Development Goals](https://mlsanity.com/paper/2505.24528/) (2025)
- [Data Smells in Public Datasets](https://mlsanity.com/paper/2203.08007/) (2022)
- [Optimizing AI-Assisted Code Generation](https://mlsanity.com/paper/2412.10953/) (2024)
- [The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities](https://mlsanity.com/paper/2506.14829/) (2025)
- [Operationalizing AI for Good: Spotlight on Deployment and Integration of AI Models in Humanitarian Work](https://mlsanity.com/paper/2507.15823/) (2025)
- [Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review](https://mlsanity.com/paper/2509.12034/) (2025)

## Abstract (arXiv)

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.