How Nonprofits are Using AI

What is it about?

This chapter explains how philanthropic organizations in the United States are engaging with artificial intelligence, machine learning, and data science, and it does so by systematically mapping who is involved and how their missions differ. Drawing on data from several major sources of nonprofit, media, and grantmaking information, the author identifies 349 distinct philanthropic organizations that are connected in some way to these technologies and then sorts them into three main categories based on how central technology is to their purpose. The first group is described as tech-centered organizations whose very mission is to directly support the development or advancement of AI, machine learning, or data science tools and research. The second group is labeled tech-perpetuating, meaning these organizations focus on expanding access to, or building communities and infrastructure around, AI and related fields rather than building the core technologies themselves. The third group is called tech-implementing, where AI, machine learning, or data science are used as tools within broader philanthropic efforts, such as improving program effectiveness, targeting resources, or addressing social problems more efficiently. To make these distinctions concrete, the chapter presents case studies of 15 organizations that illustrate each category, showing the range of ways philanthropic missions can intersect with advanced data technologies. It closes by proposing a conceptual framework for understanding the role of philanthropy in both advancing technological innovation and steering that innovation toward public benefit, offering a vocabulary and structure that researchers and practitioners can use to think about how philanthropy shapes, and is shaped by, AI, machine learning, and data science.

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Photo by Hitesh Choudhary on Unsplash

Photo by Hitesh Choudhary on Unsplash

Why is it important?

This chapter is important because it provides one of the first systematic maps of how philanthropic organizations are engaging with artificial intelligence, machine learning, and data science, turning a scattered and fast-changing field into something that can be clearly seen and studied. By identifying hundreds of organizations and then carefully grouping them into tech-centered, tech-perpetuating, and tech-implementing missions, it offers a shared language that helps funders, nonprofits, and researchers distinguish between very different ways of “doing AI” in philanthropy instead of treating them as all the same. This matters practically because it shows funders where activity is already dense and where there may be gaps, helps nonprofits see models for using data and AI in socially beneficial ways, and alerts policymakers and critics to the real structures through which private money is shaping the direction of emerging technologies. The chapter’s use of concrete case studies also makes the discussion more accessible, showing how abstract debates about AI ethics and social impact are tied to specific organizations, strategies, and grant decisions that can be copied, questioned, or improved. Finally, by ending with a conceptual framework for the role of philanthropy in advancing technology and improving society, it lays groundwork for future research and practice, encouraging more intentional, transparent, and accountable use of AI-related tools in the public interest rather than letting these developments unfold without scrutiny.