Machine learning is increasingly being recognized as a strategic resource for advancing the social missions of nonprofit organizations (NPOs), and not just as a technical tool. As Toosi et al. (2020) argue, advances in machine learning and artificial intelligence open up concrete opportunities to address complex social problems, in line with the United Nations Sustainable Development Goals. For civil society and NPOs, the central question therefore becomes how to integrate machine learning in order to amplify social development, strengthen accountability, and protect vulnerable groups.<\/p>\n\n\n\n
At its core, machine learning refers to computational methods that learn patterns from data to classify, predict, or recommend outcomes. Holzer (2023) shows how supervised learning models can predict donor behavior with high accuracy, allowing NGOs to anticipate repeat donations and prioritize engagement efforts more efficiently. Complementarily, Wiepking and colleagues demonstrate that methodologies supported by machine learning tools help organizations identify which factors most strongly drive donation and program participation, offering a finer understanding than that provided by traditional linear models. These applications allow organizations to move from a purely descriptive logic to a predictive and prescriptive logic in fundraising and program management.<\/p>\n\n\n\n
Emerging empirical studies also highlight how machine learning can support strategic management in the nonprofit sector. A recent study comparing linear regression with supervised learning approaches in predicting organizational reputation and volunteer numbers concludes that techniques such as Random Forests and Gradient Boosting outperform traditional models in terms of predictive accuracy (Opening the Black Box of Nonprofit Reputation and Volunteer Attraction, 2025). The authors show that financial indicators and governance practices are particularly relevant predictors of reputation, suggesting that machine learning can help leaders understand which levers are most decisive in sustaining public trust. This type of insight can inform board-level decisions on transparency, financial communication, and volunteer engagement strategies.<\/a>\u200b<\/p>\n\n\n\n Evidence from practice also suggests that data science and machine learning can transform how NGOs measure and communicate impact. Martinez (2024) argues that the systematic use of data allows organizations to reach the populations that need it most, democratize access to effective intervention models, and create a \u201cvirtuous circle\u201d in which better evidence generates more support and, in turn, more capacity for innovation. Case studies show organizations using data science techniques to refine participant recruitment strategies and improve program design, enabling them to access more stable funding and experiment iteratively. Convergingly, Addend Analytics (2023) documents partnerships in which data science techniques help NGOs in low-resource contexts predict which water sources are most viable and monitor long-term outcomes, strengthening community autonomy and donor trust.<\/p>\n\n\n\n In the broader \u201cAI for Social Good\u201d movement, machine learning is understood as a collaborative effort between technologists and domain experts. Toosi et al. (2020) emphasize that the most impactful projects tend to emerge from long-term partnerships that anchor ML tools in local knowledge, ethical safeguards, and well-defined social objectives. Their synthesis highlights applications ranging from agriculture to public health and digital rights, showing, for example, how ML-based systems can support human moderators in identifying online abuse or detecting diseases early in agricultural crops. For NGOs and civil society organizations, this reinforces the idea that technical performance alone is insufficient; legitimacy depends on inclusive governance, transparency about the limitations of the models, and ongoing dialogue with the affected communities.<\/p>\n\n\n\n In parallel, significant barriers to adoption persist. Holzer (2023) notes that many NGOs face constraints related to data quality, fragmented information systems, and limited internal capabilities, which can compromise the reliability and practical usefulness of machine learning models. The literature on evidence-based practices in the non-profit sector emphasizes that analytical tools should be integrated into the organizational culture; this implies empowering frontline teams and managers to interpret and act on results, rather than viewing data analysis as an external auditing exercise (Martinez, 2024). Without this attention to internal dynamics, machine learning risks being reduced to a succession of isolated pilot projects with little lasting effect on social outcomes.<\/p>\n\n\n\n Looking to the future, the potential for social development of machine learning in nonprofit organizations seems to depend on three interconnected conditions. First, organizations need to cultivate data literacy at all levels of governance so that board members, managers, and technicians can interrogate models, challenge assumptions, and co-design appropriate uses (Holzer, 2023; Toosi et al., 2020). Second, nonprofit organizations should prioritize use cases where ML adds value to issues that are truly mission-critical\u2014such as service targeting, understanding participation factors, or predicting social risks\u2014and rigorously evaluate them (Opening the Black Box of Nonprofit Reputation and Volunteer Attraction, 2025; Martinez, 2024). Thirdly, the sector must maintain a continuous focus on fairness, representativeness, and accountability, ensuring that marginalized communities have a voice in how data is collected and algorithms are used (Toosi et al., 2020). Under these conditions, machine learning can become a true engine of social development: a means of rethinking how civil society understands problems, allocates resources, and builds more just and resilient communities.<\/p>\n\n\n\n References<\/strong><\/p>\n\n\n\n Addend Analytics. (2023). Data science and NGOs: A partnership for progress<\/em>Addend Analytics. https:\/\/addendanalytics.com\/blog\/data-science-and-ngos-a-partnership-for-progress<\/a><\/a>\u200b<\/p>\n\n\n\n Holzer, J. (2023). Machine learning for nonprofit organizations<\/em>. Brigham Young University ScholarsArchive. https:\/\/scholarsarchive.byu.edu\/cgi\/viewcontent.cgi?article=1117&context=joni<\/a><\/a>\u200b<\/p>\n\n\n\n Martinez, J. (2024, September 24). Evidence can restore and transform the nonprofit sector<\/em>. MDRC. https:\/\/www.mdrc.org\/work\/publications\/evidence-can-restore-and-transform-nonprofit-sector<\/a><\/a>\u200b<\/p>\n\n\n\n Opening the Black Box of Nonprofit Reputation and Volunteer Attraction. (2025). Nonprofit Management & Leadership<\/em>Advanced online publishing. https:\/\/ifp.nyu.edu\/2025\/journal-article-abstracts\/nml-70033<\/a><\/a>\u200b<\/p>\n\n\n\n Toosi, AN, et al. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications<\/em>, 11, 2468. https:\/\/www.nature.com\/articles\/s41467-020-15871-z<\/a><\/p>\n\n\n\n Wiepking, P., & colleagues. (n.d.). A machine-learning tool-supported methodology for nonprofit organizations<\/em>. Southern Adventist University KnowledgeExchange. https:\/\/knowledge.e.southern.edu\/cgi\/viewcontent.cgi?article=1351&context=crd<\/a><\/a>\u200b<\/p>\n\n\n\n Spotlight on Poverty & Opportunity. (2024, September 17). Evidence can restore and transform the nonprofit sector<\/em>. https:\/\/spotlightonpoverty.org\/spotlight-exclusives\/evidence-can-restore-and-transform-the-nonprofit-sector<\/a><\/a>\u200b<\/p>","protected":false},"excerpt":{"rendered":" O machine learning est\u00e1 a ser cada vez mais reconhecido como um recurso estrat\u00e9gico para avan\u00e7ar as miss\u00f5es sociais das organiza\u00e7\u00f5es sem fins lucrativos (OSFL), e n\u00e3o apenas como um adere\u00e7o t\u00e9cnico. Como defendem Toosi et al. (2020), os avan\u00e7os em machine learning e intelig\u00eancia artificial abrem oportunidades concretas para enfrentar problemas sociais complexos, em […]<\/p>","protected":false},"author":1,"featured_media":15675,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-15674","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/wecare.center\/wp-content\/uploads\/2026\/02\/Reframing-Machine-Learning-as-a-Driver-of-Social-Development-in-Non%E2%80%91Profit-Organisations.jpg?fit=1600%2C896&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/posts\/15674","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/comments?post=15674"}],"version-history":[{"count":1,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/posts\/15674\/revisions"}],"predecessor-version":[{"id":15676,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/posts\/15674\/revisions\/15676"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/media\/15675"}],"wp:attachment":[{"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/media?parent=15674"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/categories?post=15674"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wecare.center\/en\/wp-json\/wp\/v2\/tags?post=15674"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}