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# Biased Sampling

If the model already has an initial bias, it could lead to bias degradation over time. This can occur if the data set used to train the model relies on decisions made by the tainted model.

Imagine a “Hiring HR” ML model biased towards male applicants. If the model is discriminatory, fewer female applicants will be hired. Even if there are higher success rates for hired female applicants, after the model processes the input data, there will be fewer samples from the discriminated population, and as a result fewer women will be considered for employment.