Teaching Paper: Key AI and Machine Learning Concepts for Non-Profit Practitioners
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-03
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About the Article
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly vital to non-profit innovation, but technical terminology often acts as a barrier. This Teaching Paper provides a glossary-based guide to foundational and advanced AI/ML concepts, linking definitions directly to their applications in non-profit practice. It explains essential terms such as algorithms, neural networks, supervised and unsupervised learning, decision trees, clustering, and regression models, while also introducing advanced topics such as GANs, federated learning, and reinforcement learning. Each concept is contextualized with applied examples relevant to non-profit operations, from donor segmentation to humanitarian logistics. By translating technical language into accessible, practice-oriented explanations, this resource supports scholars and practitioners in responsibly integrating AI into mission-driven work.
Key Topics
- Foundational AI/ML concepts and terminology
- Data processes: collection, cleaning, annotation, normalization
- Core models: regression, decision trees, random forests, SVMs
- Neural networks, CNNs, RNNs, and GANs
- Advanced approaches: reinforcement and federated learning
- Practical applications for non-profit program delivery and evaluation
Suggested Citation
Kazanskaia, A. N. (2025). Teaching Paper: Key AI and Machine Learning Concepts for Non-Profit Practitioners. NEYA Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-tp-03
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