AI systems advising or making high-stakes decisions in healthcare, criminal justice, and infrastructure may encode biases, fail catastrophically on distribution shift, or be misused by bad actors.
AI systems are increasingly embedded in high-stakes decisions — medical diagnosis, criminal sentencing, credit scoring, child welfare assessments — yet systematic evaluation of their accuracy, fairness, and failure modes is rare. This report documents known cases of AI decision-making failures and biases, evaluates the accountability gap, and proposes technical and regulatory frameworks for responsible deployment.