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Re-envisioning the Role of “Big Data” in the Nonprofit Sector: A Data Feminist Perspective

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Abstract

Use of big data in the nonprofit sector is on the rise as a part of a trend toward “data-driven” management. While big data has its critics, few have addressed fundamental ontological and epistemological issues big data presents for the nonprofit sector. In this article, we address some of these issues including most prominently the notion that big data are value neutral and divorced from context. Drawing on data feminism, an intersectional feminist framework focusing on critically interrogating our experience with data and data-driven technologies, we examine the power differentials inherent in the construction of big data and challenge the claims, priorities, and inequities it produces specifically for nonprofit work. We conclude the article with a call for nonprofit scholars and practitioners to employ a data feminist framework to harness the power of big (and small) data for justice, equity, and co-liberation through nonprofit work.

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References

  • Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., & Robinson, D. G. (2020). Roles for computing in social change. In Proceedings of the 2020 conference on fairness, accountability, and transparency, (pp. 252–260).

  • Araz, O. M., Choi, T., Olson, D. L., & Salman, F. S. (2020). Data analytics for operational risk management. Decision Sciences, 51(6), 1316–1319. https://doi.org/10.1111/deci.12443

    Article  Google Scholar 

  • Benjamin, R. (2020). Race after technology: Abolitionist tools for the new Jim Code. Social Forces, 98(4), 1–3. https://doi.org/10.1093/sf/soz162

    Article  Google Scholar 

  • Bopp, C., Harmon, E., & Voida, A. (2017). Disempowered by data: Nonprofits, social enterprises, and the consequences of data-driven work. In Proceedings of the 2017 CHI conference on human factors in computing systems, (pp. 3608–3619).

  • boyd, D., & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

    Article  Google Scholar 

  • Brimacombe, T., Kant, R., Finau, G., Tarai, J., & Titifanue, J. (2018). A new frontier in digital activism: An exploration of digital feminism in Fiji. Asia & the Pacific Policy Studies, 5(3), 508–521. https://doi.org/10.1002/app5.253

    Article  Google Scholar 

  • Buolamwini, J. & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 1–15.

  • Burns, R., & Thatcher, J. (2015). Guest editorial: What’s so big about big data? Finding the spaces and perils of big data. GeoJournal, 80(4), 445–448. https://doi.org/10.1007/s10708-014-9600-8

    Article  Google Scholar 

  • Chandy, R., Hassan, M., & Mukherji, P. (2017). Big data for good: Insights from emerging markets. Journal of Product Innovation Management, 34(5), 703–713.

    Article  Google Scholar 

  • Chowdhury, R. & Mulani, N. (2018, Oct 24). Auditing algorithms for bias. Harvard Business Review. Retrieved online at https://hbr.org/2018/10/auditing-algorithms-for-bias

  • Cinnamon, J. (2020). Data inequalities and why they matter for development. Information Technology for Development, 26(2), 214–233. https://doi.org/10.1080/02681102.2019.1650244

    Article  Google Scholar 

  • Cintron, D. W., & Montrosse-Moorhead, B. (2021). Integrating big data into evaluation: R code for topic identification and modeling. American Journal of Evaluation. https://doi.org/10.1177/10982140211031640

    Article  Google Scholar 

  • Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. MIT Press.

    Book  Google Scholar 

  • D’Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6–18.

    Google Scholar 

  • D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

    Book  Google Scholar 

  • Dastin, J. (2018, October). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.

  • Dencik, L., Hintz, A., Redden, J., & Treré, E. (2019). Exploring data justice: Conceptions, applications and directions. Information, Communication & Society, 22(7), 873–881.

    Article  Google Scholar 

  • Desouza, K. C., & Jacob, B. (2017). Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043–1064.

    Article  Google Scholar 

  • Desouza, K. C., & Smith, K. L. (2014). Big data for social innovation. Stanford Social Innovation Review, 12(3), 38–43.

    Google Scholar 

  • Duval, A. M., Gendron, Y., & Roux-Dufort, C. (2015). Exhibiting nongovernmental organizations: Reifying the performance discourse through framing power. Critical Perspectives on Accounting, 29, 31–53. https://doi.org/10.1016/j.cpa.2015.01.003

    Article  Google Scholar 

  • Dzieza, J. (2019). Why CAPTCHAs have gotten so difficult. The Verge.

    Google Scholar 

  • Eikenberry, A. M. (2009). Refusing the market: A democratic discourse for voluntary and nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 38(4), 582–596.

    Article  Google Scholar 

  • Espinoza, M. I., & Aronczyk, M. (2021). Big data for climate action or climate action for big data? Big Data & Society, 8(1), 1–15. https://doi.org/10.1177/2053951720982032

    Article  Google Scholar 

  • Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. Picador.

  • Feit, M. E., & Sandberg, B. (2022). The dissonance of “doing good”: Fostering critical pedagogy to challenge the selective tradition of nonprofit management education. Public Integrity. https://doi.org/10.1080/10999922.2022.2034341

    Article  Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

    Article  Google Scholar 

  • Goldkind, L., Wolf, L., & LaMendola, W. (2021). Data justice: Social work and a more just future. Journal of Community Practice, 29(3), 237–256.

    Article  Google Scholar 

  • Gray, J. (2019). Data witnessing: Attending to injustice with data in Amnesty International’s decoders project. Information, Communication & Society, 22(7), 971–991. https://doi.org/10.1080/1369118X.2019.1573915

    Article  Google Scholar 

  • Grønbjerg, K. A., & Salamon, L. M. (2012). Devolution, marketization, and the changing shape of government-nonprofit relations. In L. M. Salamon (Ed.), The state of nonprofit America (pp. 549–586). Brookings Institution Press.

    Google Scholar 

  • Guo, C., & Saxton, G. D. (2020). The quest for attention: Nonprofit advocacy in a social media age. Stanford University Press.

    Google Scholar 

  • Hasenfeld, Y., & Garrow, E. E. (2012). Nonprofit human-service organizations, social rights, and advocacy in a neoliberal welfare state. Social Service Review, 86(2), 295–322.

    Article  Google Scholar 

  • Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Elsevier.

    Book  Google Scholar 

  • Ji, M. (2022). Technology for whom? Owning our platforms. Nonprofit Quarterly. Retrieved from https://nonprofitquarterly.org/technology-for-whom-owning-our-platforms/

  • Kanter, B., & Paine, K. D. (2012). Measuring the networked nonprofit: Using data to change the world. John Wiley & Sons.

    Google Scholar 

  • Kempeneer, S. (2021). A big data state of mind: Epistemological challenges to accountability and transparency in data-driven regulation. Government Information Quarterly, 38(3), 101578. https://doi.org/10.1016/j.giq.2021.101578

    Article  Google Scholar 

  • Kinsella, B. (2021). Data science for social good volunteer motivations and limitations: An exploratory survey. Chance, 34(3), W86–W95.

    Article  Google Scholar 

  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.

    Google Scholar 

  • Klievink, B., Neuroni, A., Fraefel, M., & Zuiderwijk, A. (2017). Digital strategies in action: A comparative analysis of national data infrastructure development. In: Proceedings of the 18th annual international conference on digital government research, (pp. 129–138).

  • Kumar, Y., Sood, K., Kaul, S., & Vasuja, R. (2020). Big data analytics and its benefits in healthcare. In A. J. Kulkarni, P. Siarry, P. K. Singh, A. Abraham, M. Zhang, A. Zomaya, & F. Baki (Eds.), Big data analytics in healthcare (pp. 3–21). Springer.

    Chapter  Google Scholar 

  • Lake, R. W. (2017). Big Data, urban governance, and the ontological politics of hyperindividualism. Big Data & Society. https://doi.org/10.1177/2053951716682537

    Article  Google Scholar 

  • Luckett, L. (2018, Feb 16). Small data for big impact. Stanford Social Innovation Review. Retrieved from https://ssir.org/articles/entry/small_data_for_big_impact

  • Mason, D. P., McDougle, L., & Jones, J. A. (2019). Teaching social justice in nonprofit management education: A critical pedagogy and practical strategies. Administrative Theory & Praxis, 41(4), 405–423.

    Article  Google Scholar 

  • McFarland, D. A., & McFarland, H. R. (2015). Big data and the danger of being precisely inaccurate. Big Data & Society. https://doi.org/10.1177/2053951715602495

    Article  Google Scholar 

  • Meden, B., Rot, P., Terhorst, P., Damer, N., Kuijper, A., Scheirer, W. J., Ross, A., Peer, P., & Struc, V. (2021). Privacy-Enhancing face biometrics: A comprehensive survey. IEEE Transactions on Information Forensics and Security, 16, 4147–4183. https://doi.org/10.1109/TIFS.2021.3096024

    Article  Google Scholar 

  • Mendes, K., Ringrose, J., & Keller, J. (2019). Digital feminist activism: Girls and women fight back against rape culture. Oxford University Press. https://doi.org/10.1093/oso/9780190697846.001.0001

    Article  Google Scholar 

  • Meng, A., & DiSalvo, C. (2018). Grassroots resource mobilization through counter-data action. Big Data & Society. https://doi.org/10.1177/2053951718796862

    Article  Google Scholar 

  • Miner, J. D. (2020). Informatic tactics: Indigenous activism and digital cartographies of gender-based violence. Information, Communication & Society. https://doi.org/10.1080/1369118X.2020.1797851

    Article  Google Scholar 

  • Mirabella, R. M. (2013). Toward a more perfect nonprofit. Administrative Theory & Praxis, 35(1), 81–105.

    Article  Google Scholar 

  • Mirabella, R. M., & Nguyen, K. (2019). Educating nonprofit students as agents of social transformation: Critical public administration as a way forward. Administrative Theory & Praxis, 41(4), 388–404.

    Article  Google Scholar 

  • Mittelstadt, B. (2016). Auditing for transparency in content personalization systems. International Journal of Communication, 10(0), 4991–5002 https://ijoc.org/index.php/ijoc/article/view/6267

  • Never, B., & de Leon, E. (2014). The effect of government contracting on nonprofit human service organizations: Impacts of an evolving relationship. Human Service Organizations: Management, Leadership & Governance, 38(3), 258–270.

    Google Scholar 

  • Post, M., & Dodge, J. (2019). The promise of qualitative and participatory approaches to performance assessment: A critical perspective. In A. M. Eikenberry, R. M. Mirabella, & B. Sandberg (Eds.), Reframing nonprofit management: Democracy, inclusion, and social change (pp. 138–153). Melvin & Leigh.

    Google Scholar 

  • Ryzhov, I. O., Han, B., & Bradic, J. (2016). Cultivating disaster donors using data analytics. Management Science, 62(3), 849–866. https://doi.org/10.1287/mnsc.2015.2149

    Article  Google Scholar 

  • Salamon, L. (1995). Partners in public service: Government-nonprofit relations in the modern welfare state. John Hopkins University Press.

    Book  Google Scholar 

  • Salido-Andres, N., Rey-Garcia, M., Alvarez-Gonzalez, L. I., & Vazquez-Casielles, R. (2021). Mapping the field of donation-based crowdfunding for charitable causes: Systematic review and conceptual framework. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 32(2), 288–302.

    Article  Google Scholar 

  • Sandberg, B., Elliot, E., & Petchel, S. (2020). Investigating the marketization of the nonprofit sector: A comparative case study of two nonprofit organizations. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 31, 494–510. https://doi.org/10.1007/s11266-019-00159-8

    Article  Google Scholar 

  • Sandberg, B., & Russo, A. (Forthcoming). Oppression, exploitation and possibilities for emancipation under neoliberalism: Nonprofit organizing and voluntary action from the view of critical theory and postmodernism. In R.M. Mirabella, T. Coule, & A.M. Eikenberry (Eds.), The handbook of critical perspectives on nonprofit organizing and voluntary action: Concepts, applications and future directions. Edward Elgar Publishing.

  • Sawicki, D. S., & Craig, W. J. (1996). The democratization of data: Bridging the gap for community groups. Journal of the American Planning Association, 62(4), 512–523.

    Article  Google Scholar 

  • Schmitz, H. P., Dedmon, J. M., Bruno-van Vijfeijken, T., & Mahoney, J. (2020). Democratizing advocacy?: How digital tools shape international non-governmental activism. Journal of Information Technology & Politics, 17(2), 174–191.

    Article  Google Scholar 

  • Shevin, M., Shabazz, A., & Pfefferkorn, M. (2022, July 14). Co-powering an emergent horizon. Stanford Social Innovation Review. Retrieved from https://ssir.org/articles/entry/co_powering_an_emergent_horizon#

  • Stone, D. (2012). Policy paradox: The art of political decision-making. W.W. Norton & Company.

    Google Scholar 

  • Swaminathan, J. M. (2018). Big data analytics for rapid, impactful, sustained, and efficient (RISE) humanitarian operations. Production and Operations Management, 27(9), 1696–1700.

    Article  Google Scholar 

  • Van der Voort, H. G., Klievink, A. J., Arnaboldi, M., & Meijer, A. J. (2019). Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making? Government Information Quarterly, 36(1), 27–38.

    Article  Google Scholar 

  • Voida, A., Harmon, E., & Al-Ani, B. (2012). Bridging between organizations and the public. In Proceedings of the SIGCHI conference on human factors in computing systems, (pp. 1967–1976). https://doi.org/10.1145/2207676.2208341

  • Vydra, S., & Klievink, B. (2019). Techno-optimism and policy-pessimism in the public sector big data debate. Government Information Quarterly, 36(4), 101383.

    Article  Google Scholar 

  • Yeung, D., Balebako, R., Gaviria, C. I. G., & Chaykowsky, M. (2020). Face recognition technologies: Designing systems that protect privacy and prevent bias. RAND Corporation. https://doi.org/10.7249/RR4226

    Article  Google Scholar 

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Authors and Affiliations

  1. Portland State University, P.O. Box 751, Portland, OR, 97207-0751, USA

    Billie Sandberg & Andrew Russo

  2. University of North Dakota, Grand Forks, USA

    Laura C. Hand

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  1. Billie Sandberg
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  2. Laura C. Hand
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Correspondence to Billie Sandberg.

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Sandberg, B., Hand, L.C. & Russo, A. Re-envisioning the Role of “Big Data” in the Nonprofit Sector: A Data Feminist Perspective. Voluntas 34, 1094–1105 (2023). https://doi.org/10.1007/s11266-022-00529-9

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