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.

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.

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.

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.

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

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.

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.

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).

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.

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.

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

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.

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.

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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