Cover of The Routledge Handbook of Artificial Intelligence and Philanthropy
As artificial intelligence (AI) continues to advance across domains, questions remain around how philanthropies can harness the latest AI innovations within the grant making cycle, how data can support responsible AI development, and the role of philanthropy in the AI ecosystem.
Towards this end, the Routledge Handbook of Artificial Intelligence and Philanthropy, edited by Giuseppe Ugazio and Milos Maricic, examines the ways in which AI and philanthropy can support each other. Set for release on November 7, 2024, this handbook gathers insights from AI researchers, philanthropy scholars, and other practitioners to provide frameworks and models for practitioners to consider as they approach AI adoption in their work. The handbook also includes resources on how AI and data intersect in the context of philanthropy.
The book covers a range of topics starting with foundational discussions on AI applications in philanthropic organizations. The authors present case studies on how various philanthropies are integrating data science and AI in their work, detailing the differences in adoption levels and technological readiness. They also explore how philanthropy can influence the development of AI, emphasizing the role of ethical standards, inclusivity, and collaboration. The concluding sections of the book include various topics involved in responsible AI deployment such as risk management and regulatory issues.
Three Key Takeaways for the DATA4Philanthropy Network:
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Responsible AI in Philanthropy: The handbook highlights philanthropy’s role in promoting responsible AI development and deployment, emphasizing that AI initiatives should be inclusive and socially aligned, which is essential for organizations dedicated to responsible data use.
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Using AI in Philanthropy: Drawing from examples, the handbook examines the ways in which AI can contribute to impact assessments and data-informed decision-making, aiding in grantmaking processes and project evaluations.
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Risk Management: The text outlines strategies for managing AI-related risks and introduces a framework for understanding data security, ethics, and accountability in AI implementation, guiding organizations in mitigating challenges associated with incorporating AI.
The handbook can be found here.
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