Artificial Intelligence and the Nonprofit Sector: A Working Bibliography
Artificial Intelligence has a long history in the science of information and communications technology. The recent emergence of programs like CHAT-GPT have captivated the public mind and raised fear and promises of a new world of either wonder or horror or both. Artificial Intelligence has a long history in both Information and communication technology and popular culture.
While Artificial Intelligence is not new technology, novel variations emerge all of the time. Early work dates back at least until the 1950s (some argue for the 1930s) and the development of classical AI--applications like expert systems and related concepts. The subsequent developments include Machine Learning, Artificial neural networks, deep neural networks and recruent nural networks (Pieraccini, 2021). Large Language models and Generative AI have gained in popularity in recent years.
The nonprofit sector can benefit from many of the capacities that AI has to offer. Applications in planning, data collection and analysis, advocacy, fundraising, communications and so many other aspects of nonprofit life are possible.
It is important to note that many traditional forms of technology have artificial intelligence components incorporated. While AI Tools have great potential for the nonprofit sector, there are technical and ethical issues that are important to consider.
This bibliography is offered as a service to those who want to study ad learn, It will never be comprehensive nor will it be completely current. The field is generating too much literature too quickly.
AI and Nonprofit Bibliography
Ahn, M. J., & Chen, Y. C. (2020, June). Artificial intelligence in government:
potentials, challenges, and the future. In The 21st Annual International
Conference on Digital Government Research (pp. 243-252).
Alperstein, N. (2021). Exploring Issues of Social Justice and Data Activism: The
Personal Cost of Network Connections in the Digital Age. In
Performing Media
Activism in the Digital Age (pp. 143-177). Palgrave Macmillan, Cham.
Alsoibi, I., Agarwal, R., Bharathy, G., Samarawickrama, M., Unhelkar, B., & Prasad, M. (2023). A Systematic Review and Taxonomy of Data Analytics in Non-profit Organizations. Asia Pacific Journal of Information Systems (APJIS).
Baek, T. H., Bakpayev, M., Yoon, S., & Kim, S. (2022). Smiling AI agents: How anthropomorphism and broad smiles increase charitable giving. International Journal of Advertising, 41(5), 850-867.
Balidemaj, V. (2024). Artificial Intelligence Integration, Concerns, Benefits, and the Need for Ethical Policies for Community Foundations and Nonprofit Organizations. Dissertation/Clemson University https://open.clemson.edu/all_dissertations/3697?utm_source=open.clemson.edu%2Fall_dissertations%2F3697&utm_medium=PDF&utm_campaign=PDFCoverPages
Bankhwal, M., Chiu, M., Bisht, A., Roberts, R., & van Heteren, A. (2024). AI for social good: Improving lives and protecting the planet. McKinsey & Company. Available online: https://www. mckinsey. com/capabilities/quantumblack/our-insights/ai-for-social-good
Barenblat, K., & Gosselink, B. H. (2024). Mapping the Landscape of AI-Powered Nonprofits. Stanford Social Innovation Review. https://doi.org/10.48558/A85X-DE19
Barzanti, L., Benvenuti, L., & Gaudenzi, E. (2024). The impact of artificial intelligence and information technology on philanthropic organizations: Case studies of non-profit and cultural associations. In The Routledge Handbook of Artificial Intelligence and Philanthropy (pp. 107-131). Routledge.
Belfield, H. (2020, February). Activism by the AI community: Analysing recent
achievements and future prospects. In Proceedings of the AAAI/ACM Conference
on AI, Ethics, and Society (pp. 15-21).
Benjamin, R. (2019). Assessing risk, automating racism.
Science, 366(6464), 421-422.
Bondi, E., Xu, L., Acosta-Navas, D., & Killian, J. A. (2021, July). Envisioning communities: A participatory approach towards AI for social good. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 425-436).
Berendt, B. (2019). AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing. Paladyn, Journal of Behavioral Robotics, 10(1), 44-65.
Buolamwini, J. (2016). The algorithmic justice league.
Medium (December,
2016),<
https://medium.
com/mit-media-lab/the-algorithmic-justice-league-3cc4131c5148>
Calhoun, S. R., & Holzer, A. (2024). Enhancing Nonprofit Efficiency and Productivity through AI: Applications of ChatGPT. Journal of Nonprofit Innovation, 4(2), 3.
Cave, S., & Dihal, K. (2020). The whiteness of AI. Philosophy & Technology, 33(4),
685-703.
Ceccaroni, L., Bibby, J., Roger, E., Flemons, P., Michael, K., Fagan, L., & Oliver, J. L. (2019). Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice, 4(1), Article-number.
Chen, Y. C., Ahn, M. J., & Wang, Y. F. (2023). Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector. Sustainability, 15(6), 4796.
Chui, M., Harryson, M., Valley, S., Manyika, J., & Roberts, R. (2018).
Notes from the AI frontier applying AI for social
good.
https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good
Cihon, P., Schuett, J., & Baum, S. D. (2021). Corporate governance of artificial intelligence in the public interest. Information, 12(7), 275.
Cornebise, J., Worrall, D., Farfour, M. & Marin, M.(2018). Witnessing atrocities: quantifying villages destruction in Darfur with crowdsourcing and transfer learning. In Proc. AI for Social Good NeurIPS2018 Workshop, NeurIPS ’18,Montreal, Canada (2018).
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society, 1-25.
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). A definition, benchmark and database of AI for social good initiatives. Nature Machine Intelligence, 3(2), 111-115.
Dardis, F. E., Buckley, C., Mitra, P., Heaton, C., & Xiao, A. (2025). Improving nonprofit engagement on social media: Using big data, machine-learning, and sentiment analysis to evaluate leading nonprofits’ message strategies on Twitter/X. International Journal of Information Technology, 17(1), 395-407.
Efthymiou, I. P., Egleton, T. W. E., Chatzivasileiou, S., & Emmanouil-Kalos, A. (2023). Artificial intelligence and the future for charities. International Journal of Non-Profit Sector Empowerment, 2(1), e35345-e35345.
Ely, T..Calabrese,T. & Jung, J/ (2023). Research implications of electronic filing of nonprofit information: Lessons from the United States’ Internal Revenue Service Form 990 series. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations 34.(1), 20-28.
Eubanks, V. (2018).
Automating inequality: How high-tech tools profile,
police, and punish the poor. St. Martin's Press.
Farmer, J., McCosker, A., Albury, K., & Aryani, A. (2023). Data for Social Good: Non-Profit Sector Data Projects (p. 127). Springer Nature.
Feldstein, S. (2019). The road to digital unfreedom: How artificial intelligence
is reshaping repression. Journal of Democracy, 30(1), 40-52.
Fruchterman, J. (2016). Using data for action and for impact. Stanford Social Innovation Review, 14(3), 30.
Fyall, R., Moore, M. K., & Gugerty, M. K. (2018). Beyond NTEE codes: Opportunities to understand nonprofit activity through mission statement content coding. Nonprofit and Voluntary Sector Quarterly, 47(4), 677-701.
Garhart, N., & Rowland, C. (2023). It wasn't me, it was the AI: Intellectual property and data privacy concerns with nonprofits' use of artificial intelligence systems. Board & Administrator for Administrators Only, 40(4), 1-2.
Goldkind, L., & McNutt, J. G. (2019). We could be unicorns: Human services
leaders Moving from Managing Programs to Managing Information Ecosystems.
Human Service Organizations: Management, Leadership & Governance,
43(4),
269-277.
Goldkind, L. (2021). Social Work and Artificial Intelligence:
Into the Matrix. Social Work.,
https://doi.org/10.1093/sw/swab028
Hines, A. K. (2024). Pioneering Nonprofit AI Initiatives: A Guide to Generational Diversity, Ethics, and Collaboration. A. Kay Publishing, LLC.
Inclezan, D., & Pradanos, L. I. (2017). A critical view on smart cities and AI.
Journal of Artificial Intelligence Research,
60, 681-686.
Iskandarova, S., & Sloan, M. F. (2023, December). Exploring Nonprofit and Government Agency AI Policies and Regulations: Systems Leadership. In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) (pp. 197-203). IEEE.
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data
governance: Organizing data for trustworthy Artificial Intelligence. Government
Information Quarterly, 37(3), 101493.
Jones, M., McCabe, E., & Olson, R. (2023). Identifying essential nonprofits with a novel NLP Method. Nonprofit Management and Leadership, 33(3), 661-674.
Kanter, B., & Fine, A. H. (2022). The smart nonprofit: staying human-centered in an automated world. John Wiley & Sons.
Kanter, B., Fine, A., & Deng, P. (2023). 8 Steps Nonprofits Can Take to Adopt AI Responsibly. Stanford Social Innovation Review. https://doi.org/10.48558/VTXC-GR37
Kim, Y. (2022). Personality of nonprofit organizations’ Instagram accounts and its relationship with their photos’ characteristics at content and pixel levels. Frontiers in Psychology, 13, 923305.
Krafft, P. M., Young, M., Katell, M., Lee, J. E., Narayan, S., Epstein, M., ...
& Barghouti, B. (2021, March). An Action-Oriented AI Policy Toolkit for
Technology Audits by Community Advocates and Activists. In
Proceedings of the
2021 ACM Conference on Fairness, Accountability, and Transparency (pp.
772-781).
Kshirsagar, M., Robinson, C., Yang, S., Gholami, S., Klyuzhin, I., Mukherjee, S., ... & Lavista Ferres, J. M. (2021, July). Becoming good at AI for good. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 664-673).
Lang, B. (2024). Digital philanthropy in China: How internet fundraising platforms and artificial intelligence are transforming non-profit governance. In The Routledge Handbook of Artificial Intelligence and Philanthropy (pp. 221-239). Routledge.
Laynor, G. (2021). Artificial Whiteness: Politics and Ideology in Artificial
Intelligence by Yarden Katz. Information & Culture, 56(3), 356-357.
Lee, G., Pippy, J., & Hobbs, M. (2022, December). Optimizing the Feature Set for Machine Learning Charitable Predictions. In Australasian Joint Conference on Artificial Intelligence (pp. 631-645). Cham: Springer International Publishing.
Lee, M. K., Kim, J. T., & Lizarondo, L. (2017, May). A human-centered approach to algorithmic services: Considerations for fair and motivating smart community service management that allocates donations to non-profit organizations. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3365-3376).
Lee, H., Wang, X., & Dull, R. B. (2021). Designing a Classifying System for Nonprofit Organizations Using Textual Contents from the Mission Statement. Journal of Information Systems, 1-26.
Ma, J., Ebeid, I. A., de Wit, A., Xu, M., Yang, Y., Bekkers, R., & Wiepking, P. (2021). Computational social science for nonprofit studies: Developing a toolbox and knowledge base for the field. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 1-12.
Ma, J. (2021). Automated coding using machine learning and remapping the US nonprofit sector: A guide and benchmark. Nonprofit and Voluntary Sector Quarterly, 50(3), 662-687.
Mate, A., Madaan, L., Taneja, A., Madhiwalla, N., Verma, S., Singh, G., ... & Tambe, M. (2022, June). Field study in deploying restless multi-armed bandits: Assisting non-profits in improving maternal and child health. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 11, pp. 12017-12025).
Matsumura, N., & Sasaki, Y. (2006). Leader qualification in managing nonprofit organization. In New Frontiers in Artificial Intelligence: Joint JSAI 2005 Workshop Post-Proceedings (pp. 411-419). Springer Berlin Heidelberg.
Mayer, L. H. (2019). The promises and perils of using big data to regulate nonprofits. Wash. L. Rev., 94, 1281.
McBride, K., van Noordt, C., Misuraca, G., & Hammerschmid, G. (2021). Towards a
Systematic Understanding on the Challenges of Procuring Artificial Intelligence
in the Public Sector.
https://doi.org/10.31235/osf.io/un649
McNutt, J.G. (2018) (ed).
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McNutt, J.G., Guo, C., Goldkind, L., & An, S. (2018). Technology in Nonprofit
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Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2021). Artificial Intelligence
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Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021).
Algorithmic fairness: Choices, assumptions, and definitions.
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Musikanski, L., Rakova, B., Bradbury, J., Phillips, R., & Manson, M. (2020).
Artificial intelligence and community well-being: A proposal for an emerging
area of research. International Journal of Community Well-Being, 3(1),
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Najibi, A. (2020). Racial discrimination in face recognition technology. Harvard
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Noble, S. U. (2018). Algorithms of oppression. New York University Press.
Pohl, B., & Goldkind, L. (2023). AI folk tales: how nontechnical publics make sense of artificial intelligence. In Research Handbook on Artificial Intelligence and Communication (pp. 246-266). Edward Elgar Publishing.
Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where responsible AI
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Ren, C., & Bloemraad, I. (2022). New Methods and the Study of Vulnerable Groups: Using Machine Learning to Identify Immigrant-Oriented Nonprofit Organizations. Socius, 8, 23780231221076992.
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Santamarina, F. J., Lecy, J. D., & van Holm, E. J. (2021). How to code a million missions: Developing bespoke nonprofit activity codes using machine learning algorithms. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 1-10.
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