The bias problem with artificial intelligence, and how to solve it by Sanjay Srivastava via @infomgmt

AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias.

I agree - you need good quality and representative training data if you want to get good results from any AI and ML you want to use. My advice would be:

1.  Take your time - rushing always leads to mistakes so be realistic with plans.
2.  Be careful with the methodology you use to create and split your data into Training and Data.
3.  Try to use separate teams to test the same piece of code - the hope being that it will help to avoid the bias. Think of it as a human version of a small parallel ML solution.
4.  Check, check and check again.