Garbage In, Garbage Out (GIGO)

GIGO emphasizes that the quality of an AI or machine learning system depends heavily on the quality of the data it receives. If training data contains errors, inconsistencies, missing information, or biases, the model may learn those problems and reflect them in its predictions. The same principle can apply to inputs provided to an AI system during use.

Advanced models cannot automatically compensate for every problem in their underlying data. Poor inputs can reduce accuracy, reinforce bias, and create unreliable results. Maintaining high-quality data through cleaning, validation, monitoring, and governance is therefore essential for dependable AI systems.