Dataset Bias refers to systematic errors or skews in training data that cause models to learn unintended, misleading patterns — the model captures the bias in the data rather than the true underlying relationship, leading to poor generalization and fairness issues.

Common Dataset Biases

• Selection Bias: The data is not representative of the real-world distribution — sampling is skewed.
• Label Bias: Labels are systematically wrong for certain subgroups — annotator bias or measurement bias.
• Representation Bias: Certain groups, conditions, or scenarios are underrepresented in the dataset.
• Measurement Bias: The features or labels are measured differently for different subgroups.

Why It Matters

• Fairness: Dataset bias is the primary cause of algorithmic unfairness — biased data produces biased models.
• Generalization Failure: Models trained on biased data fail when deployed on the true distribution.
• Semiconductor: Training data from a single fab, tool, or time period creates bias toward those specific conditions.

Dataset Bias is garbage in, garbage out — systematic data errors that cause models to learn the wrong patterns instead of the true signal.