Evaluate fairness and accuracy metrics across demographic groups is correct.

Evaluate fairness and accuracy metrics across demographic groups means computing and comparing performance and fairness measures for each racial group so you can detect disparate impact or unequal error rates. You should look at accuracy, false positive and false negative rates, calibration and group specific metrics and then assess whether observed differences are statistically meaningful and acceptable for your use case.

This evaluation should include checks for small sample sizes and intersectional subgroups so you do not miss harms that affect a subset of the population. If you find disparities you can apply mitigation techniques such as reweighting, resampling, fairness constrained training or post processing and then re evaluate the metrics to confirm improvement.

Vertex AI Model Evaluation is incorrect because a product or tool by itself does not confirm absence of bias unless you actually measure and compare fairness metrics across groups. The tool can help but the correct approach is to perform explicit fairness and accuracy evaluations.

Omit the race attribute from the training data is incorrect because removing the sensitive attribute does not prevent the model from learning proxies for race and it also prevents you from measuring whether outcomes differ by race. You need the attribute or a reliable proxy to evaluate group level performance and to guide mitigation.

Train the model using data from a single racial group is incorrect because training on a single group will not generalize and will likely produce highly biased outcomes for other groups. That approach increases discrimination rather than preventing it.

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