Operationalizing AI for Good: Spotlight on Deployment and Integration of AI Models in Humanitarian Work
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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.
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References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impactcited by 18
- 2021PLM: Partial Label Masking for Imbalanced Multi-label Classificationcited by 10
- 2021Becoming Good at AI for Goodcited by 8
- 2023Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Goodcited by 6
- 2024"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learningcited by 5
- 2024HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aidcited by 1
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