Teaching Paper: Data Sets and Code Resources for Applied AI in Non-Profit Practice
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-ai-mch-lr-tp-02
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About the Article
The growing availability of open datasets and code repositories has significantly lowered the barriers to artificial intelligence (AI) and machine learning (ML) adoption, particularly for non-profits operating in resource-constrained environments. This Teaching Paper presents a curated overview of data sources such as Kaggle, UCI Machine Learning Repository, and Google Dataset Search, alongside repositories of code examples including TensorFlow, PyTorch, and Hugging Face. A case study demonstrates how a small NGO adapted open datasets and scikit-learn code examples to predict student dropout risk, showing how accessible resources can drive practical social impact. The discussion emphasizes challenges of data relevance, ethics, and sustainability, offering practical guidance for academics and practitioners. By linking technical tools with social good, the paper contributes to both digital inclusion and responsible AI practice.
Key Topics
- Open datasets for AI and ML experimentation
- Code repositories and pre-trained model hubs
- Applications for non-profit problem-solving
- Case study: student dropout prediction in South Asia
- Ethical challenges of open data use
- Strategies for contextual adaptation and sustainability
Suggested Citation
Kazanskaia, A. N. (2025). Teaching Paper: Data Sets and Code Resources for Applied AI in Non-Profit Practice. NEYA Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-tp-02
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