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Noelle Russell's Scaling Responsible AI: From Enthusiasm to Execution presents two frameworks, LEAD AI and SECURE AI, designed to help organizations move from initial AI experiments to responsible, large-scale deployment. Drawing on her two-decade career at IBM, AWS, Amazon Alexa, and Microsoft, Russell breaks the AI lifecycle down into practical checklists, “Takeaways,” and “Reflection Questions” for leaders, innovators, and practitioners. This review argues that while the frameworks and checklists offer a teachable, cross-functionally accessible language for launching a responsible-AI initiative, the book has three notable shortcomings: (1) its evidence base relies heavily on anecdote rather than peer-reviewed research; (2) its chapter on bias and fairness identifies problems effectively but does not equip readers to measure or address them; and (3) its guidance is oriented primarily toward the budgets and staffing of large, U.S.-based enterprises, with little consideration for smaller organizations, the public sector, or the Global South. Overall, Scaling Responsible AI is a practical, workshop-ready guide to responsible AI at the enterprise level, but readers seeking technical depth, documented evidence, or a more globally inclusive perspective will need to look beyond this book.
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