AI capability is advancing. Trust must catch up.
Most AI systems sound equally confident whether they are right or wrong. That is not a bug. It is how they are built. And it is the problem that organisations deploying AI in consequential decisions have not yet solved.
The Correctness Problem traces how the gap between AI capability and AI trustworthiness became the defining challenge of intelligent system deployment. Drawing on cases from aviation, medicine, finance, and law, the book examines why AI systems hallucinate with confidence, why scale amplifies rather than reduces the problem, and why the governance frameworks most organisations have adopted offer the appearance of accountability without its substance.
The argument is structural, not technical. AI systems do not need to be smarter. The organisations deploying them need to ask different questions - about what the system is authorised to decide, what happens when it is wrong, and who is accountable when no one has drawn that boundary explicitly.
The Correctness Problem introduces the discipline of deterministic intelligence: the architecture of systems defined not by what they can produce, but by what they are permitted to influence. A system that cannot refuse is not constrained. It is merely slowed.
For leaders. For governance professionals. For everyone accountable when things go wrong.
