National security decisions pose a paradox: they are among the most important actions a government can take, yet they are generally the least transparent to the democratic public. The "black box" nature of national security decision-making—driven by extensive classification and characterized by difficulty overseeing executive actions—has expanded in the United States as executive power continues to grow.

The rise of artificial intelligence (AI) systems to enhance national security decision-making—or even to make autonomous decisions—deepens this challenge because it is difficult to understand how AI algorithms, often described as "black boxes," reach their conclusions. The widespread use of AI within the national security ecosystem makes U.S. national security choices even more opaque to the public, congressional overseers, U.S. allies, and even the executive officials making the decisions. How can we be confident that the U.S. government's use of these AI systems aligns with our values, including rationality, lawfulness, and accountability?

The Double Black Box: National Security, Artificial Intelligence, and the Struggle for Democratic Accountability addresses these urgent challenges. As China is committed to becoming the world leader in AI and faces fewer legal and value-based restrictions on its development of military AI, democracies' commitment to using AI in lawful and ethical ways will be tested. This book defines and explores the "double black box" phenomenon and then identifies ways that policymakers, military and intelligence officials, and lawyers in democratic countries like the United States can benefit from advanced technologies without compromising their public law values.

Pages: 272, Dimensions: 15.5x15.5cm