The hidden nature of black box AI creates several important problems. Firstly, these models are hard to trust because users can't see how they work or verify their reasoning. Secondly, when errors happen, they're difficult to fix because developers can't easily identify what went wrong inside the model.

Black box models can hide bias. They might make unfair decisions based on race, gender, or other factors without anyone noticing. For example, AI hiring systems have rejected qualified women because they were trained on data from mostly male hires.

These models may also fail unpredictably in new situations they weren't trained for. Finally, black box models make it hard to meet regulations that require companies to explain important decisions about loans, insurance, or employment.