Tech Fixx · Video
Garbage In, Garbage Out: How Data Quality Shapes AI Results
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“Garbage in, garbage out” means the quality of a system’s output is limited by the information and assumptions entering it. This beginner AI lesson compares useful data with poor-quality data and shows why cleaning alone cannot fix every problem.
Good data should be: • Relevant to the task • Sufficiently accurate and complete • Consistent and well documented • Representative of the intended population • Collected and used responsibly
Bad data may contain missing values, duplicates, incorrect labels, hidden bias, obsolete information or measurements that do not match the real goal.
Quality is always purpose-dependent: the same dataset may be suitable for one task and unsafe for another.
#GarbageInGarbageOut #DataQuality #AIExplained