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.

 

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.

Alsolbi, I., Agarwarl, R., Narayan, B., Bharathy, G., Samarawickrama, M., Tafavogh, S., & Prasad, M. (2022). Analyzing Donors Behaviors in Nonprofit Organizations: A Design Science Research Framework. In Pattern Recognition and Data Analysis with Applications (pp. 765-775). Singapore: Springer Nature Singapore.

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.

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).

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.

Benjamin, R. (2019). Assessing risk, automating racism. Science, 366(6464), 421-422.

Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Power to the people? opportunities and challenges for participatory AI. Equity and Access in Algorithms, Mechanisms, and Optimization, 1-8.https://doi.org/10.1145/3551624.3555290

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>

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.

Cave, S., & Dihal, K. (2020). The whiteness of AI. Philosophy & Technology33(4), 685-703.

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.

Ely, Todd L., Thad D. Calabrese, and Jihye Jung. "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 (2023): 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 Democracy30(1), 40-52.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2021). How to design AI for social good: seven essential factors. Ethics, Governance, and Policies in Artificial Intelligence, 125-151.

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.

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

Gong, Z., Li, X., Liu, J., & Gong, Y. (2019). Machine learning in explaining nonprofit organizations’ participation: a driving factors analysis approach. Neural Computing and Applications, 31, 8267-8277.

Inclezan, D., & Pradanos, L. I. (2017). A critical view on smart cities and AI. Journal of Artificial Intelligence Research, 60, 681-686.

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.

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).

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). Technology, Activism and Social Justice in a Digital Age. London & New York:  Oxford University Press. 

McNutt, J.G., Guo, C., Goldkind, L., & An, S. (2018). Technology in Nonprofit organizations and voluntary action. Voluntaristics Review. 3(1) 1-63.

Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2021). Artificial Intelligence in Government: Taking Stock and Moving Forward. Social Science Computer Review, 08944393211034087.

Mehr, H. (2017). Artificial intelligence for citizen services and government. Ash Cent. Democr. Gov. Innov. Harvard Kennedy Sch., no. August, 1-12.

Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021). Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application, 8, 141-163.

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-Being3(1), 39-55.

Najibi, A. (2020). Racial discrimination in face recognition technology. Harvard University.https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/

Noble, S. U. (2018). Algorithms of oppression. New York University Press.

Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-23.

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.

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.

Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). Using data mining techniques to explore security issues in smart living environments in Twitter, Computer Communications. https://doi.org/10.1016/j.comcom.2021.08.021

Singh, R., Sonewar, P., Kumar, M., Shingare, A., Deshpande, A., Satyam, K., ... & Colorafi, K. J. (2022, December). Empowering Nonprofit Organization to Reduce Donation Attrition with Machine Learning. In 2022 IEEE Pune Section International Conference (PuneCon) (pp. 1-4). IEEE.

Pérez-Morote, R., Pontones-Rosa, C., & Núñez-Chicharro, M. (2020). The effects of e-government evaluation, trust and the digital divide in the levels of e-government use in European countries. Technological Forecasting and Social Change, 154, 119973.

Repede, Ș. E. (2023). Researching disinformation using artificial intelligence techniques: challenges. BULLETIN OF" CAROL I" NATIONAL DEFENCE UNIVERSITY, 12(2), 69-85.

Shekhtman, L. M., Gates, A. J., & Barabási, A. L. (2022). Mapping Philanthropic Support of Science. arXiv preprint arXiv:2206.10661.

Shi, Z. R., Wang, C., & Fang, F. (2020). Artificial intelligence for social good: A survey. arXiv preprint arXiv:2001.01818.

Tashea, J. (2017). Tenant Power: Nonprofits provide legal and tech support to help resolve housing disputes. ABA Journal, 103(7), 16-18.

Tipnis, V. S., Yoo, E., Urrea, G., & Gao, F. (2024). AI-Powered Philanthropy: Effects on Volunteer Productivity. Available at SSRN 4701631.

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., ... & Clopath, C. (2020). AI for social good: unlocking the opportunity for positive impact. Nature Communications, 11(1), 2468.

Tripp, W., Gage, D., & Williams, H. (2020). Addressing the data analytics gap: A community-university partnership to enhance analytics capabilities in the non-profit sector. Collaborations: A Journal of Community-Based Research and Practice, 3(1).

Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., & Ignacio Criado, J. (2019, June). A review of artificial intelligence in government and its potential from a public policy perspective. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 91-99).

Verhulst, S. G. (2018). Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern. AI & society33, 293-297.

Wang, X., Lee, H., & Dull, R. (2021). Predicting the Discontinuity of Non-Profit Organizations Using the Machine Learning Approach. Available at SSRN 3780096.

Wang, Z., Yan, R., Chen, Q., & Xing, R. (2010). Data mining in nonprofit organizations, government agencies, and other institutions. International Journal of Information Systems in the Service Sector (IJISSS), 2(3), 42-52.

Wasif, R. (2021). Terrorists or persecuted? The portrayal of Islamic nonprofits in US newspapers post 9/11. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 32(5), 1139-1153.

West, D. M., & Allen, J. R. (2018). How artificial intelligence is transforming the world. Report. April, 24, 2018 Brookings Institution.

West, D. M., & Allen, J. R. (2020). Turning Point: Policymaking in the Era of Artificial Intelligence. Brookings Institution Press.

West, D. M. (2018). The future of work: Robots, AI, and automation. Brookings Institution Press.

Winschiers-Theophilus, H., Zaman, T., & Stanley, C. (2019). A classification of cultural engagements in community technology design: introducing a transcultural approach. Ai & Society34(3), 419-435.

Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818-829.

Wong, F., de la Fuente-Nunez, C., & Collins, J. J. (2023). Leveraging artificial intelligence in the fight against infectious diseases. Science, 381(6654), 164-170.

Završnik, A. (2019). Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of Criminology, 1477370819876762.

Züger, T., & Asghari, H. (2023). AI for the public. How public interest theory shifts the discourse on AI. AI & SOCIETY, 38(2), 815-828.

 

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