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AI Alignment books explore how to design, evaluate, and govern AI systems so they reliably pursue human-intended goals and values. This category spans technical methods such as reward design and modeling, preference learning and human feedback, interpretability, robustness to distribution shift, scalable oversight, corrigibility, and preventing specification gaming. It also addresses philosophical and ethical questions—what values to align to, how to handle moral uncertainty and pluralism—and analyzes risks from near-term harms to long-term existential concerns. Governance and policy play a central role, including standards, auditing, accountability, red teaming, and global coordination. Works range from research monographs and textbooks to primers, critiques, and roadmaps, often with case studies and debates about feasibility, trade-offs, and societal impact. Aimed at engineers, researchers, policymakers, ethicists, and informed readers, the focus is not on making AI more capable, but on making it trustworthy, controllable, and beneficial.

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