Discussions around responsibly applying probabilistic models to decision making at a societal level are really important to have.
Myth-making about ML as a shadowy conspiracy—instead of just another tool for engineering, like databases—is counter-productive.
The "main use case" for ML is the literal one, "designing models that can learn to predict high probability outcomes in situations where a deterministic model cannot be applied." It is another technology in an engineer's toolkit. Look at your smartphone, and you will see that the majority of apps you use rely on machine learning for ETA prediction, content recommendation, speech-to-text, image processing, translation, spam filtering, etc.
Myth-making about ML as a shadowy conspiracy—instead of just another tool for engineering, like databases—is counter-productive.
The "main use case" for ML is the literal one, "designing models that can learn to predict high probability outcomes in situations where a deterministic model cannot be applied." It is another technology in an engineer's toolkit. Look at your smartphone, and you will see that the majority of apps you use rely on machine learning for ETA prediction, content recommendation, speech-to-text, image processing, translation, spam filtering, etc.