Machine Learning requires data to identify data patterns with which to develop predictions. If the input data is biased (for example it is referencing design data that has consistent errors) then this bias will be apparent in to predictions/solutions it provides. Of course there also needs to be enough data for the Machine Learning algorithm to identify patterns with sufficient confidence. If this is lacking then the outputs may be unreliable.