AI systems often fail when applied to real-world problems. An automated loan system may deny an application due to an address format error. The human behind the application has perfect credit. The system sees data, not context.
These systems train on typical, clean datasets. They struggle with exceptions. They lack the judgment of a human. A customer service bot cannot grasp emotional distress. A medical AI misses rare, atypical symptoms. This punishes legitimate users.
Design AI for human exceptions. Add human oversight for edge cases. Build robust feedback loops for system improvement.