Some of the points are true specifically the last part but you are wrong about the fact that people who write these software doesn't understand what patterns ar

Some of the points are true specifically the last part but you are wrong about the fact that people who write these software doesn't understand what patterns are being found. We can clearly see in ML and Deep models why the decision was made by the hypothesis using various libraries such as eli5, Tensorboard and others. Deep Learning models are in general harder to debug but still possible. Therefore we know why hypothesis produces wrong results but sometimes it not possible to mend the model due to outliers, rare events, lack of data and/or randomness that surrounds our world. Just as you point out that statistical analysis is done by people who don’t really understand the math, these false result can be due to scientists using ML without understanding its advantages and limitations.