
Overview
When AI Goes Wrong
Real-world AI failures and their consequences: From hiring bias to autonomous vehicle crashes
FailuresCase Studies

Understanding AI risks from security vulnerabilities to algorithmic bias. Learn about adversarial attacks, data privacy, model safety, and ethical concerns.

Real-world AI failures and their consequences: From hiring bias to autonomous vehicle crashes

Unfair AI: how biased training data and design choices lead to discriminatory outcomes

When AI confidently generates false information: Understanding model confabulation

Protecting personal information: privacy risks in AI training and inference

Synthetic media threats: AI-generated content that erodes truth and trust

Threats to democratic processes: AI-powered disinformation campaigns

Automation impact: how AI affects employment and the future of work

Military AI: the risks of delegating life-and-death decisions to machines