Section 1

Responsible AI Foundations

Responsible AI begins before model selection. A team must know what workflow it is changing, who benefits, who can be harmed, which authority the system receives, what evidence can reveal unequal outcomes, and how an affected person obtains correction. The chapters progress from normative purpose to empirical diagnosis to an explicit decision rule. Ethics defines what matters. Bias analysis investigates systematic distortion and its mechanism. Fairness turns a named harm into a justified comparison, metric set, threshold policy, monitoring plan, and remedy.