Why Do AI Systems Misbehave?
CSET Georgetown Danny Hague
AI misbehavior results from interactions among five components: training data quality, the model's training objective, neural architecture design, system-level guardrails and sampling strategies, and conversation context. Data biases account for a large portion of failures—a medical imaging model once relied on hospital watermarks rather than anatomy, and a horse-to-zebra converter added zebra stripes to rider clothing. Identifying which component causes misbehavior enables targeted fixes, though improvements in one area can sometimes create vulnerabilities elsewhere.
Why it matters
AI systems are increasingly impressive, which makes their failures all the more baffling. This blog dives into the causes of AI misbehavior. The post Why Do AI Systems Misbehave? appeared first on Center for Security and Emerging Technology.
