See Model Bias as a System Problem
Explain why model bias usually reflects choices in problem framing, data, and evaluation rather than a single broken prediction.
The reframe: models do not float above organizational history. They ingest it. Data selection already makes a fairness argument Who appears in the dataset, who is missing, and what gets measured all shape the model's world. If the sample does not reflect the population that will face the prediction, the system starts misaligned. Labels can encode past inequality A model cannot tell whether the target column is a fair proxy for the outcome you really care about. If labels come from subjective ratings, complaint rates, or unequal enforcement, the model may learn those patterns as if they were ground truth.…
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