This book arrives at a time when AI has shifted from being just a supplementary digital tool to becoming a fundamental infrastructure that shapes how organizations allocate resources, evaluate impact, and justify decisions. For nonprofit scholarship, this change is especially important because philanthropy functions both as an allocative institution and a moral domain. Consequently, decisions must be defensible on technical and normative grounds. The book aims to be an early attempt to explore this new intersection and build a field that is just beginning to take shape. The editors’ backgrounds strengthen the book’s concept. Ugazio’s work in behavioral philanthropy and finance, grounded in philosophy and neuroeconomics, emphasizes the cognitive and moral dimensions of giving. Maricic’s experience in AI-driven philanthropic centers on implementation, decision-making, and impact strategies. They view AI as a shift in socio-technical governance, not just an ethical risk or a neutral tool.

The volume starts with AI in philanthropy, then regional readiness, followed by philanthropy’s influence on AI policies, and concludes with ethical issues. It shifts focus from technology adoption to rethinking philanthropic decision-making, aligning with research that digital transformation mainly changes governance and accountability, not just communication. The core analytical insight in the operational chapters suggests that AI shifts epistemic authority. When machine learning models classify needs, evaluate grant proposals, or forecast donor actions, they do more than just speed up tasks; they incorporate new standards for relevance and credibility. What is accepted as convincing evidence or measurable impact becomes partly embedded within these models. As explanations shift from expert judgment to statistical inference, the framework for accountability changes. This aligns with recent research emphasizing transparency and clear methodology as essential for trustworthy nonprofit knowledge. While AI boosts analysis, it also creates opacity and debate over the fairness of decisions.

The claim that AI transforms philanthropy would be more convincing with clearer theoretical backing. The chapters present practical uses like screening, segmentation, and impact measurement, but lack specific details on the organizational changes involved. Does AI primarily facilitate managerial rationalization through standardization and audits? Does it alter strategy by emphasizing data-driven decisions? Or does it challenge legitimacy by transferring authority to algorithmic outputs? While the book mentions “algorithmic philanthropy,” it does not fully explore this idea as a complete theory of algorithmic governance. This gap highlights the need for further engagement with emerging research on computational approaches in the nonprofit sector.

The regional analyses are some of the most convincing contributions. They show that AI adoption, influenced by infrastructure, regulations, and resources, varies across philanthropic ecosystems. Rather than spreading evenly, AI often widens existing gaps: well-funded foundations formalize data-driven systems, while smaller groups risk marginalization. These insights align with broader studies on how organizations adapt to technological challenges.

The section arguing that philanthropy can shape AI is intellectually ambitious. By viewing philanthropic organizations as governance players capable of affecting the ethical course of AI via funding, advocacy, and regulation, the book broadens philanthropic theory from mere resource distribution to meta-governance. Foundations are seen as entities that shape the rules guiding the development of socio-technical systems. However, this perspective needs more evaluation. If philanthropy plays a stewardship role in AI governance, its legitimacy must be proven, especially regarding private influence and accountability. Digital inequality and issues of civic authority show that technological governance relies on institutional legitimacy.

The ethical chapters address bias, transparency, and donor influence as fundamental dilemmas rather than minor risks. Predictive personalization in fundraising can boost efficiency but also raise issues about manipulation and distributional bias. These tensions are seen as governance questions: who audits algorithms, who can challenge outcomes, and how conflicts of value are resolved. These issues connect with emerging analyses of AI’s impact across nonprofit management areas.

Methodologically, the edited format allows for breadth but results in uneven analytical depth. Some chapters provide strong conceptual insights, while others are more descriptive. Increasing synthesis by clearly defining shared concepts, such as algorithmic accountability and computational legitimacy, would enhance overall theoretical coherence. Nonetheless, as an initial effort to consolidate the field, the volume effectively establishes AI philanthropy as a legitimate interdisciplinary area and highlights the tensions that scholars need to address rather than delay.

In sum, The Routledge Handbook of Artificial Intelligence and Philanthropy is well-timed and thoughtfully organized. Its most lasting insight is that AI transforms not only philanthropic methods but also philanthropic authority, how decisions are justified, institutionalized, and challenged. By shifting focus from adoption to governance, the book urges nonprofit scholarship to examine who controls data, who defines impact, who audits models, and how legitimacy is maintained when philanthropic judgment becomes partly computational.