AAISM states that the strongest control against bias is ensuring representative, diverse, and inclusive training data. This directly minimizes skew and systemic underrepresentation.
Complex architectures (B) do not reduce bias and may exacerbate it. Reducing updates (C) increases drift. More labels (D) improves supervision quality but does not inherently solve bias if the data itself is skewed.
[References: AAISM Study Guide – Bias Mitigation and Data Quality Requirements., , ]
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