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2. If you dataset was biased, why did you end up using it as-is? What are your processes to screen for data bias and correct it? What biases are you watching out for?
3. In the event that you end up training a model on a biased dataset: will QA catch the model's biases before it makes it into production? Does your QA process even take ML bias into account?
These are not data problems -- these are organizational and cultural problems. The fact that a biased dataset caused an issue is actually the outcome of the entire system. Team diversity will help with these things, organically, but having formal processes is necessary by now.
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