This guide covers the law governing automated decision systems in the United States, which is not primarily artificial intelligence law at all. It begins with the framework that actually applies: the domain in which the decision is made, whether the inputs are consumer reports, whether reasons must be disclosed, and whether outcomes fall unevenly on protected groups. Disclosure chapters cover adverse action notices under the credit and consumer reporting statutes, the specificity requirement, and risk-based pricing notices. Discrimination chapters cover disparate treatment, disparate impact and its burden-shifting framework, proxy variables, and the less discriminatory alternative analysis that decides most model cases. Later chapters cover the state profiling rules and bias audit mandates, explainability as a set of distinct technical problems, model documentation and validation, vendor contracting, production monitoring, due process for government determinations, insurance and health coverage decisions, litigating these cases, and a governance program.