Abstract: In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which “there is no scientific basis on which to form any calculable probability whatever.” Knight claimed that “Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated.” Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D, and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real. Dogs face Knightian uncertainty; horses and elephants face it; human beings face it; in particular, human beings who make policy, or develop regulations, sometimes face it. Knightian uncertainty poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario (and thus to adopt the maximin rule), but serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential “miracles,” or (3) create uncertain risks.