# Operationalizing AI for Good: Spotlight on Deployment and Integration of AI Models in Humanitarian Work

arXiv: https://arxiv.org/abs/2507.15823 · HTML: https://mlsanity.com/paper/2507.15823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [PLM: Partial Label Masking for Imbalanced Multi-label Classification](https://mlsanity.com/paper/2105.10782/) (2021)
- [Becoming Good at AI for Good](https://mlsanity.com/paper/2104.11757/) (2021)
- [Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good](https://mlsanity.com/paper/2305.05471/) (2023)
- ["We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning](https://mlsanity.com/paper/2403.16795/) (2024)
- [HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid](https://mlsanity.com/paper/2410.06370/) (2024)

## Abstract (arXiv)

Publications in the AI for Good space have tended to focus on the research and model development that can support high-impact applications. However, very few AI for Good papers discuss the process of deploying and collaborating with the partner organization, and the resulting real-world impact. In this work, we share details about the close collaboration with a humanitarian-to-humanitarian (H2H) organization and how to not only deploy the AI model in a resource-constrained environment, but also how to maintain it for continuous performance updates, and share key takeaways for practitioners.